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Washout Failure

Prime #
1273
Origin domain
Epistemology And Inference
Subdomain
experimental design → Epistemology And Inference
Aliases
Carryover Contamination, Insufficient Settling Interval

Core Idea

Washout failure is the structural pattern in which, when the same unit is observed under successive conditions, the second observation is contaminated by residual state from the first because no gap long and clean enough to dissipate that state was built into the design. It is the design defect of underestimating the dissipation time, so that what is read as an effect of the new condition is partly the still-decaying tail of the old one.

The structure has a fixed shape across substrates. A unit is subjected to two or more conditions in sequence. The first condition induces a state that persists past the condition that caused it, decaying on a characteristic timescale. A measurement of the second condition is taken before that decay is effectively complete. The resulting estimate is therefore a mixture — the second condition's true effect plus the residual of the first. The essential commitment is that clean sequential measurement requires a latent equivalence: the unit must be in the same state at the start of the second condition as it was at the start of the first, and the washout interval is the operational proxy for that equivalence. The interval must be set to several decay constants of the carryover process, not to a convenient round number, because the contamination falls only as fast as the induced state itself decays. The hidden assumption the pattern surfaces is that the unit returns to a common baseline between trials; washout failure is what happens when that assumption is asserted rather than secured.

How would you explain it like I'm…

The Spicy Cracker Trick

If you eat a super spicy chip and then right away taste a plain cracker, the cracker tastes spicy too — but only because the burn from the chip hasn't gone away yet. You have to wait for your mouth to cool down before the cracker can taste like itself. Not waiting long enough fools you about how the cracker really tastes.

Not Waiting Long Enough

Washout failure happens when you test the same thing under one condition and then another, but you don't leave enough of a gap in between. The first condition leaves behind some leftover state that slowly fades, and if you measure the second condition too soon, that leftover is still there. So your reading is really a mix: the second condition's true effect plus the fading tail of the first. The fix is a 'washout' gap long enough for the leftover to disappear — and how long that takes depends on how fast the leftover actually fades, not on picking a tidy round number.

Leftover State Contamination

Washout failure is the design defect where, when the same unit is observed under successive conditions, the second observation is contaminated by residual state from the first because no gap long and clean enough to dissipate that state was built in. The structure is fixed: a unit goes through two or more conditions in sequence; the first induces a state that persists past its cause and decays on some characteristic timescale; a measurement of the second condition is taken before that decay is effectively complete; so the estimate is a mixture of the second condition's true effect plus the residual of the first. Clean sequential measurement requires a latent equivalence — the unit must be in the same state at the start of the second condition as at the start of the first — and the washout interval is the operational proxy for that equivalence. The interval must be set to several decay constants of the carryover process, not a convenient round number, because contamination falls only as fast as the induced state decays. The hidden assumption the pattern exposes is that the unit returns to a common baseline between trials — and washout failure is what happens when that is asserted rather than secured.

 

Washout failure is the structural pattern in which, when the same unit is observed under successive conditions, the second observation is contaminated by residual state from the first because no gap long and clean enough to dissipate that state was built into the design. It is the design defect of underestimating the dissipation time, so that what is read as an effect of the new condition is partly the still-decaying tail of the old one. The structure has a fixed shape: a unit is subjected to two or more conditions in sequence; the first induces a state that persists past the condition that caused it, decaying on a characteristic timescale; a measurement of the second condition is taken before that decay is effectively complete; the resulting estimate is therefore a mixture — the second condition's true effect plus the residual of the first. The essential commitment is that clean sequential measurement requires a latent equivalence: the unit must be in the same state at the start of the second condition as it was at the start of the first, and the washout interval is the operational proxy for that equivalence. The interval must be set to several decay constants of the carryover process, not to a convenient round number, because the contamination falls only as fast as the induced state itself decays. The hidden assumption the pattern surfaces is that the unit returns to a common baseline between trials; washout failure is what happens when that assumption is asserted rather than secured.

Structural Signature

a unit observed under successive conditions — an induced state persisting past the condition that caused it — a characteristic decay timescale for that state — a successor measurement taken before decay is complete — a contaminated estimate mixing new effect with old residual — a common-baseline invariant: clean measurement requires the unit in the same state at each condition's start

The pattern is present when each of the following holds:

  • A re-used unit. The same unit — patient, cohort, user pool, instrument, neural channel — is subjected to two or more conditions in sequence.
  • An induced state. The first condition leaves a residual state — drug level, carryover learning, habituation, adjustment cost, hysteresis — that persists past its cause.
  • A decay timescale. That state dissipates on a characteristic timescale (a half-life), the load-bearing quantity for setting the gap.
  • A premature measurement. The second condition is measured before the residual has effectively decayed.
  • A contaminated estimate. The resulting measurement is a mixture — the second condition's true effect plus the still-decaying tail of the first.
  • A common-baseline invariant. Clean sequential measurement requires the unit be in the same state at the start of the second condition as at the start of the first; the washout interval is the operational proxy for that latent equivalence, and must run several decay constants, not a convenient round number.

The components compose so that the design parameter is the ratio of the inter-condition interval to the carryover half-life: the structure converts an unstated return-to-baseline assumption into a checkable quantity, predicts the bias's direction and magnitude from the decay constant alone, and shows the fix is temporal rather than analytic — measuring harder at the wrong time does not help.

What It Is Not

  • Not temporal decay. temporal_decay_and_degradation is the gradual loss of a capacity; washout failure concerns the time required for an induced state to decay, with a biased measurement as the consequence — decay is the mechanism, not the failure.
  • Not hysteresis. hysteresis is a system's output depending on its input history; washout failure is a design failure to let that history dissipate before the next measurement.
  • Not confounding in general. confounding is the broad structural defect of a third factor distorting a comparison; washout failure is a specific temporal variety — carryover contamination between successive observations on the same unit.
  • Not selection bias. selection_bias concerns who is observed; washout failure concerns when successive observations are taken on the same units.
  • Not a maintenance lapse. maintenance (the nearest embedding neighbor) is sustaining a system against wear; washout failure is about an inter-condition settling interval, not upkeep.
  • Common misclassification. Reading a biased second-period estimate as a real effect of the new condition. Catch it by asking whether shortening the gap inflates the estimate and lengthening it converges to a stable value; that signature is carryover, not a genuine effect.

Broad Use

The pattern recurs across measurement substrates that share only the sequential-observation structure. In clinical trials, crossover designs require a drug washout between treatments; too short, and the second arm's effect estimate is biased by residual pharmacology. In education research, testing a curriculum immediately after a prior intervention conflates the new pedagogy with carryover learning. In product analytics and A/B testing, re-exposing the same user to a new variant before behavioral adaptation to the old variant has decayed inflates or deflates the new effect estimate. In policy evaluation, assessing a new regulation while the prior regime's induced behavior is still adjusting attributes those adjustment costs to the new policy. In sensors and metrology, an instrument that does not return to baseline between samples reports systematically biased successor readings. In neuroscience and psychophysics, stimuli presented before adaptation to a prior stimulus has decayed measure against a moving baseline. Across all of them an induced state persists past its cause, a successor measurement is taken too soon, and the estimate mixes the new effect with the old residual — the same design defect in different instruments.

Clarity

The construct surfaces a hidden assumption in any sequential measurement: that the unit returns to a common baseline between trials. Ordinary analysis attends to the conditions being compared and treats the measurements as clean reads of each; the washout construct names what must be true — and how long it must take — for that to hold. By making the baseline-return an explicit requirement with a quantity attached, it converts an unstated presupposition into a checkable design parameter.

The clarifying force is also to distinguish washout failure from its neighbors, which it superficially resembles. It is not hysteresis, the state-dependence of a system's response on its input history; washout failure is a design failure to let that history dissipate before the next measurement. It is not temporal decay or degradation, the gradual loss of a capacity; washout concerns the time required for an induced state to decay, with measurement as the consequence. It is not selection bias, which concerns who is observed; washout concerns when successive observations are taken on the same units. It is a specific temporal variety of confounding — the general structural defect — picked out by its mechanism. Naming it lets an analyst attribute a biased second-period estimate to insufficient settling rather than to a real but spurious effect of the new condition.

Manages Complexity

The construct lets a designer collapse a tangled question — "is the second-period effect real?" — into two tractable subquestions: what is the half-life of the carryover process, and is the inter-condition interval many half-lives long? That decomposition replaces an open-ended worry about contamination with two answerable quantities, and the second follows mechanically from the first once the decay constant is known or estimated.

The compression also sorts the interventions, each addressing the residual-dissipation structure at a different point. Estimate the carryover half-life with a pilot study, so the required interval can be set rather than guessed. Lengthen or randomize the inter-condition gap to several decay constants so the residual is negligible at measurement. Counterbalance the order of conditions across the sample so that whatever residual bias remains averages out rather than loading onto one condition. Switch to a between-subjects design when carryover is intractable, so no unit is measured under two conditions at all. Covariate-adjust for time-since-last-condition to model out the residual statistically when it cannot be designed out. Having the structure in hand is what lets a designer choose among these — secure the equivalence by waiting, by balancing, by separating units, or by modeling — rather than defaulting to a convenient interval and hoping.

Abstract Reasoning

Holding washout failure as a unit allows reasoning about counterfactuals at the unit level over time. The cleanly measured effect of a second condition on a given unit requires that the unit be in the same state at the start of the second condition as at the start of the first; the washout interval is the operational stand-in for that latent equivalence. This reframes a measurement question as a counterfactual one: the estimate is unbiased only if the unit's pre-second-condition state is counterfactually identical to its pre-first-condition state, and the interval is how that identity is secured in practice.

The abstraction yields a quantitative prediction available before any data are collected. Because the contamination falls at the rate the induced state decays, the residual bias at measurement is a known function of the ratio between the inter-condition interval and the carryover half-life: an interval of one half-life leaves roughly half the induced state, an interval of five leaves a few percent. Reasoning from the pattern, an analyst can therefore predict the direction and magnitude of the bias from the decay constant alone, can recognize that the fix is temporal rather than analytic (more or stricter measurement at the wrong time does not help), and can identify which designs are exposed — any within-unit sequential comparison with a slow-decaying induced state — and which are immune. The same reasoning distinguishes a genuine second-period effect from a residual artifact: if shortening the interval inflates the estimate and lengthening it shrinks the estimate toward a stable value, the difference was carryover.

Knowledge Transfer

The structural roles map across measurement substrates, and with them the interventions transfer with no translation. The unit corresponds to a patient, a student cohort, a user pool, a regulated population, an instrument, a neural channel; the induced state to residual drug concentration, carryover learning, behavioral habituation, adjustment cost, instrument hysteresis, sensory adaptation; the characteristic decay timescale to the pharmacological half-life, the forgetting curve, the habituation-decay rate, the settling time; the premature successor measurement to the too-short crossover, the immediate re-test, the next-day re-exposure; the contaminated estimate to the biased second-arm effect. Because the roles correspond, an analyst who has set a drug washout recognizes the same requirement in an A/B test or a sensor calibration.

The interventions inherit that portability, and the mappings are exact. Piloting to estimate the carryover half-life is one move whether it is a pharmacokinetic sub-study, a pilot read of a forgetting curve, or a measurement of an instrument's settling time. Lengthening or randomizing the inter-condition gap to several decay constants is the same structural act across pharmacology, behavioral experimentation, and metrology. Counterbalancing the order of conditions so residual bias averages out is identical reasoning in a crossover trial and a within-subject psychophysics study. Switching to a between-subjects design when carryover is intractable, and covariate-adjusting for time-since-last-condition, recur with the same rationale across substrates. The transfer is reliable because the residual-dissipation-time structure is substrate-neutral: the term originates in clinical-trial design, but the shape — induced state, decay process, premature re-measurement, contaminated estimate, and the design interval that restores common baseline — applies identically to sensors and neural adaptation, so a practitioner in any sequential-measurement field recognizes it once the carryover is named, even where the clinical vocabulary of "washout" does not travel.

Examples

Formal/abstract

Model the carryover process as first-order exponential decay. A unit is held under condition \(A\), which induces a state \(s\) that, after \(A\) is removed, decays as \(s(t) = s_0 e^{-t/\tau}\) with half-life \(t_{1/2} = \tau \ln 2\). The successor measurement of condition \(B\) is taken after an inter-condition interval \(\Delta\). The estimate of \(B\)'s effect is then contaminated by the residual \(s(\Delta) = s_0 e^{-\Delta/\tau}\) — the measured value is the mixture \(\hat{\theta}_B = \theta_B + s_0 e^{-\Delta/\tau}\). The design parameter is the ratio \(\Delta / t_{1/2}\), and the bias is a known function of it: at \(\Delta = t_{1/2}\) roughly half the induced state remains, at \(\Delta = 5 t_{1/2}\) only about 3% remains. The common-baseline invariant is exact — clean measurement requires \(s(\Delta) \approx 0\), i.e. the unit in counterfactually the same state at the start of \(B\) as at the start of \(A\) — and the washout interval is the operational proxy for that latent equivalence. The structure dictates the fix is temporal, not analytic: measuring \(B\) more precisely at a too-short \(\Delta\) cannot remove \(s_0 e^{-\Delta/\tau}\); only lengthening \(\Delta\) (or piloting to estimate \(\tau\), counterbalancing order so the residual averages out, or covariate-adjusting on time-since-\(A\)) reduces it. A diagnostic test falls out directly: if shortening \(\Delta\) inflates the estimate and lengthening it converges to a stable value, the difference was carryover.

Mapped back: The exponential-decay model instantiates every role — re-used unit, induced state, decay timescale \(\tau\), premature measurement at \(\Delta\), contaminated mixture estimate, and common-baseline invariant — and shows the design parameter is \(\Delta / t_{1/2}\), predicting the bias before any data are collected.

Applied/industry

In crossover clinical trials, each patient receives treatment \(A\), then treatment \(B\), and serves as their own control. If \(A\) is a drug with pharmacological half-life \(t_{1/2}\) and the washout interval between arms is too short, the second arm's effect estimate is biased by residual drug — the still-active tail of \(A\) mixed into the read of \(B\). The remedy is to set the washout to several pharmacokinetic half-lives (piloted in a sub-study), counterbalance the \(A\)-then-\(B\) versus \(B\)-then-\(A\) order across patients, or switch to a parallel-group design if carryover is intractable. The identical structure governs online A/B testing: re-exposing the same user pool to a new variant before behavioral adaptation to the old variant has decayed contaminates the new variant's lift estimate, and the fix is a washout gap matched to the adaptation-decay timescale or a between-users split so no user sees both. And in sensor metrology, an instrument that has not returned to baseline between samples — thermal drift, residual charge, hysteresis in a transducer — reports a systematically biased successor reading; the engineering remedy is a settling interval set to several time constants of the instrument's relaxation, exactly the washout discipline in a non-biological substrate.

Mapped back: Across crossover trials, A/B testing, and sensor metrology the same roles recur — a re-used unit, an induced state with a decay timescale, a premature successor measurement, and a contaminated estimate — and the same intervention family transports: pilot to estimate the half-life, set the inter-condition gap to several decay constants, counterbalance order, or separate units, because the fix is temporal rather than analytic.

Structural Tensions

T1 — Temporal Fix versus Analytic Fix (scopal). The prime's sharp claim is that the fix is temporal, not analytic — measuring harder at a too-short gap cannot remove the residual. The failure mode is precision reflex: responding to a contaminated estimate by collecting more data or tightening the measurement, which cannot subtract the carryover term. Diagnostic: would the proposed fix lengthen the inter-condition gap or model the residual, or merely sharpen the measurement at the same wrong time? Only the first two touch the contamination.

T2 — Washout Length versus Experiment Cost (temporal). Setting the gap to several decay constants secures common baseline, but long washouts inflate cost, dropout, and time-to-result, and for slow-decaying states the required gap may be infeasible. The failure mode is washout overshoot or its opposite, convenient-interval default: either waiting impractically long, or rounding to a convenient gap that leaves residual. Diagnostic: is the gap set against the estimated half-life or a calendar convenience? When the half-life makes adequate washout infeasible, switch designs rather than under-wait.

T3 — Within-Unit versus Between-Unit Design (sign/direction). Switching to a between-subjects design eliminates carryover entirely, but it sacrifices the within-unit control that made the comparison powerful — trading contamination for higher variance. The failure mode is design-switch overcorrection: abandoning a crossover whose carryover was manageable, losing statistical efficiency to avoid a bias that washout could have handled. Diagnostic: is the carryover intractable (slow decay, no feasible gap) or merely present? Between-subjects is the right move only when no washout can secure baseline, not as a default flinch from any carryover.

T4 — Counterbalancing versus Asymmetric Carryover (coupling). Counterbalancing order averages out residual bias, but it assumes the carryover is symmetric — A-into-B and B-into-A leave comparable residuals. The failure mode is asymmetric-carryover masking: counterbalancing when one direction's carryover dominates, so the average hides a directional bias rather than cancelling it. Diagnostic: is the induced state symmetric across conditions, or does one condition leave a far larger residual? Counterbalancing only cancels symmetric carryover; asymmetric carryover survives the averaging.

T5 — Carryover Half-Life versus Estimation Uncertainty (measurement). The whole design hinges on the carryover half-life, but that constant is itself estimated (from a pilot) with uncertainty, and a mis-estimated half-life sets a mis-sized gap. The failure mode is false-precision washout: setting the gap to five "half-lives" of a half-life that was wrong, leaving residual the design assumed gone. Boundary with outlier_leverage if the pilot is small. Diagnostic: how well is the half-life pinned, and is the gap set with margin for that uncertainty? A point estimate of the decay constant propagates into the adequacy of the washout.

T6 — Washout Failure versus Genuine Period Effect (sign/direction). A biased second-period estimate may be carryover, or a genuine effect of being in the second period (learning, fatigue, time trend) unrelated to the prior condition. The failure mode is carryover misattribution: blaming insufficient washout for a real period effect, or vice versa. Boundary with withdrawal_rebound and confounding generally. Diagnostic: does shortening the gap inflate and lengthening it shrink the estimate toward a stable value? That signature is carryover; a residual that is invariant to gap length is a period effect, not washout failure.

Structural–Framed Character

Washout failure sits on the structural side of the middle of the structural–framed spectrum, a mixed-structural prime with an aggregate of 0.4. Its core is a residual-dissipation-time structure — an induced state decaying on a characteristic timescale, a successor measurement taken before the decay completes, and a contaminated estimate whose bias is a known function of the ratio of the inter-condition interval to the carryover half-life — and that exponential-decay relation is substrate-neutral, holding the prime on the structural side of a vocabulary that originates in clinical-trial design.

The diagnostics split. Evaluative weight reads zero: a settling interval is neither good nor bad in itself, only adequate or too short, and the prime carries no normative loading until the contamination biases an estimate. The remaining diagnostics sit at the midpoint and carry the clinical tint. The vocabulary half-travels: "washout," "crossover," and "carryover" are clinical-trial terms a new domain must partly translate, even though the \(s(\Delta) = s_0 e^{-\Delta/\tau}\) relation underneath is bare. Institutional origin sits at experimental design, and human-practice-bound reads at the midpoint because the residual-dissipation structure applies to non-biological substrates: an instrument with thermal drift, residual charge, or transducer hysteresis that has not returned to baseline between samples reports a systematically biased successor reading, exactly the washout discipline in a non-human substrate, and neural adaptation is another. Invoking the prime half-imports a frame (set the gap to several decay constants; the fix is temporal, not analytic) and half-recognizes a contamination already present in the measurement design.

The prime's substrate reasoning lands the grade: residual-state-contamination-between-successive-observations recurs in clinical trials, education research, A/B testing, policy evaluation, sensors, and psychophysics, a design pattern that travels cleanly across measurement substrates, applying identically to sensors and neural adaptation even though the clinical vocabulary of "washout" does not. That non-biological reach, plus the bare exponential-decay model, is the mixed-structural signature — a genuinely substrate-neutral timing relation carried in a clinical-trial vocabulary the residual-dissipation core does not require.

Substrate Independence

Washout failure is a strongly substrate-independent prime — composite 4 / 5 on the substrate-independence scale. Its domain breadth is wide and its structural abstraction high: the load-bearing object is a bare timing relation — residual state from one condition dissipating exponentially, contaminating a successor observation taken before it has decayed — that commits to no medium and recurs with the same force in clinical trials (the crossover washout that is too short), education research (a curriculum tested over carryover learning), product analytics and A/B testing (a user re-exposed before adaptation has decayed), policy evaluation (a new regulation assessed while the prior regime's induced behavior is still adjusting), sensors and metrology (an instrument that does not return to baseline between samples), and neuroscience and psychophysics (stimuli presented against a still-adapting baseline). The sensor and neural-adaptation instances carry the structure with no human practice involved, and the bare exponential-decay model shows the relation is medium-neutral, which is what holds the abstraction component high. Transfer evidence is strong: the diagnostic (estimate the residual-dissipation time and ensure the inter-observation gap exceeds it) and the remedy (lengthen the settling interval, or model the carryover explicitly) carry cleanly across measurement substrates. Only the clinical-trial home term "washout" — which the sensor and metrology cases show is inessential — keeps the composite at 4 rather than 5.

  • Composite substrate independence — 4 / 5
  • Domain breadth — 4 / 5
  • Structural abstraction — 4 / 5
  • Transfer evidence — 4 / 5

Relationships to Other Primes

One-hop neighborhood: parents above, mutual partners to the right, children below.Washout Failuresubsumption: ConfoundingConfounding

Parents (1) — more general patterns this builds on

  • Washout Failure is a kind of Confounding

    Washout failure is a specific TEMPORAL variety of confounding: the confounder is the residual state of a prior condition on the same unit, decaying on a half-life, biasing a successor estimate. The file frames it as 'one particular member' of confounding.

Path to root: Washout Failure → Confounding → Bias

Neighborhood in Abstraction Space

Washout Failure sits in a moderately populated region (57th percentile for distinctiveness): it has near-neighbors but no dense thicket of synonyms.

Family — Contamination & Purification (4 primes)

Nearest neighbors

Computed from structural-signature embeddings · 2026-06-14

Not to Be Confused With

A first important confusion is with confounding, of which washout failure is a specific temporal variety. Confounding is the general structural defect in which a third factor, associated with both the treatment and the outcome, distorts the estimated effect — the broad family of "something else explains the difference." Washout failure is one particular member of that family: the confounder is the residual state of the prior condition, and the mechanism is temporal — successive observations on the same unit, taken before the induced state of the first has decayed. The distinction is load-bearing because the remedy is specific to the temporal mechanism. Generic confounding is addressed by randomization, stratification, or covariate adjustment for the third factor; washout failure is addressed by waiting — lengthening the inter-condition interval to several decay constants — or by counterbalancing order, because the confounder is not a stable covariate but a decaying residual whose magnitude is a known function of elapsed time. A practitioner who frames washout failure as generic confounding will reach for covariate adjustment when the structurally correct fix is temporal: more analysis at the wrong time cannot subtract a residual that only time removes.

A second genuine confusion is with hysteresis. Both involve a system's present behavior depending on its past, and washout failure often arises in hysteretic systems. But hysteresis is a property of the system — its output genuinely depends on the path of its input, so the same input yields different outputs depending on history. Washout failure is a property of the measurement design — a failure to allow the path-dependent state to dissipate before the next measurement is taken. Hysteresis is what makes carryover possible; washout failure is the design defect of not accounting for it. The distinction matters because hysteresis is a fact to be characterized (map the system's path-dependence), while washout failure is a defect to be corrected (build in a settling interval). A practitioner who labels a contaminated crossover estimate "hysteresis" describes the underlying physics but stops short of the actionable diagnosis — that the design failed to let the hysteretic state settle, which is fixable by lengthening the gap.

A third confusion worth drawing is with temporal_decay_and_degradation. The two share the machinery of an exponential decay process, which makes them easy to conflate. But temporal decay is about a capacity or quantity gradually being lost — performance fading, materials weakening, a signal attenuating — where the decay is the phenomenon of interest. Washout failure is about an induced state decaying so that a successor measurement is contaminated — the decay is a nuisance process whose incompleteness biases an estimate, and the object of interest is the measurement, not the decaying quantity. In temporal decay, you care about the thing that is decaying; in washout failure, you care about a different thing (the second condition's effect) that the decaying residual is corrupting. The remedies diverge accordingly: temporal decay is managed by refreshing or replacing the decaying quantity; washout failure is managed by waiting for the nuisance residual to clear before measuring. A practitioner who frames washout failure as temporal decay will attend to the residual itself rather than to the contaminated successor estimate that is the actual concern.

For a practitioner, the distinctions sort by what kind of defect is in play. If a third factor distorts a comparison, it is confounding (adjust or randomize); if the system's output genuinely depends on its input path, that is hysteresis (characterize it); if a quantity of interest is gradually being lost, it is temporal_decay_and_degradation (refresh it); and if an induced state from a prior condition contaminates a successor measurement on the same unit because the settling interval was too short, it is washout failure — the only one whose remedy is temporal: wait several decay constants, counterbalance order, or separate the units.

Solution Archetypes

No catalogued solution archetypes reference this prime yet.