{"closest_prior_art":[{"name":"Best Bike Split","overlap":"Pre-start physics-based modeling uses course, weather, aerodynamics, fitness, and rider power data to predict race time, generate course-specific power instructions, compare scenarios, and compare planned pacing with actual ride data.","remaining_difference":"The retained page does not disclose calibrated uncertainty bands, explicit energy/thermal tolerance classification, threshold-bounded redistribution, or a documented rider-coach accept/revise/reject gate with automatic model demotion.","source_ids":["SRC1"]},{"name":"RaceX","overlap":"Runs pre-race simulations using terrain, wind, road surface, aerodynamics, rider data, temperature, and humidity; segments the route; optimizes each segment's power to avoid overexertion; predicts splits; and exports the plan for execution.","remaining_difference":"The retained page does not disclose explicit thermal-strain or terminal-reserve trajectories, uncertainty-triggered fallback, an accountable pre-start commitment decision, or chronological forecast-error calibration and demotion.","source_ids":["SRC2"]},{"name":"Individualized fatigue-and-recovery optimal pacing model","overlap":"Formulates hilly time-trial pacing as a minimum-time optimal-control problem using individualized fatigue and recovery constraints, calculates course-dependent power modes, and reports simulated and pilot experimental performance comparisons.","remaining_difference":"It does not disclose the proposal's uncertainty-and-tolerance gate, thermal-strain envelope, documented rider-coach decision, or repeated forecast-to-telemetry governance loop.","source_ids":["SRC3"]},{"name":"Road-condition and corner-aware ITT optimization","overlap":"Optimizes pacing on simulated 40-km courses using slope, wind, corners, road friction, and critical-power constraints, directly modeling course-dependent allocation and unrecoverable time loss.","remaining_difference":"It is a simulation study rather than the proposed operational workflow and lacks individualized uncertainty gates, thermal predictions, accountable commitment, and retrospective recalibration.","source_ids":["SRC4"]}],"contrastive_claim_falsifier":"The contrastive claim would be falsified by documentation that an existing practice already combines, before a time-trial start, course-specific energy and thermal trajectory prediction with calibrated uncertainty, preset tolerance-triggered bounded segment-power redistribution, a recorded rider-coach accept/revise/reject decision, and linked post-race error-based recalibration or demotion; it would also fail empirically if chronological replay showed no prespecified calibration or decision-feasibility improvement over the fixed baseline.","contrastive_claim_remaining":"A narrower testable distinction remains: integrate explicit energy, thermal-strain, terminal-reserve, and uncertainty envelopes with preset redistribution bounds and a recorded rider-coach commitment gate, then use linked post-event errors to restrict or demote the model. Course-aware simulation, segment-power optimization, race-day export, scenario comparison, and planned-versus-actual review are already disclosed practices.","experiment_id":"eoa_inverse_innovation_exp13_second_slot_policy60_20260806","gates":{"adequate_source_search":{"rationale":"The bounded search covered the proposal directly, historical optimal-power and critical-power terminology, commercial pacing products and practices, and combinations of course physics, weather, fatigue, road conditions, segmentation, optimization, and retrospective comparison. Four opened direct sources from four publisher contexts were retained, including two first-party products and two primary-research reports.","source_ids":["SRC1","SRC2","SRC3","SRC4"],"status":"PASS"},"bounded_next_test":{"rationale":"A preregistered silent chronological replay is bounded, reversible, and capable of comparing calibration, tolerance classification, terminal reserve, and pre-start feasibility against a fixed baseline. Existing planned-versus-actual comparison and simulation research show that the necessary records and comparisons are technically researchable.","source_ids":["SRC1","SRC3","SRC4"],"status":"PASS"},"distinct_testable_claim":{"rationale":"Although most pacing-simulation structure collides with existing products and research, the combined uncertainty-calibrated energy/thermal envelopes, preset redistribution bounds, explicit accountable gate, and error-driven demotion remain distinct on the retained pages and yield observable process and calibration outcomes.","source_ids":["SRC1","SRC2","SRC3","SRC4"],"status":"PASS"},"no_obvious_safety_or_authority_stop":{"rationale":"The authorized first step is retrospective and silent, so it changes no racing, equipment, selection, or clinical decision. The rider, officials, and clinicians retain their stated authorities. Any later use would require safeguards because existing products can export precise segment targets for live execution, but that creates a deployment constraint rather than a stop for the proposed replay.","source_ids":["SRC1","SRC2"],"status":"PASS"},"supported_problem":{"rationale":"Course variables and physiological constraints materially affect optimal pacing, and simulation research specifically reports that time lost in slow technical sections cannot be regained later. However, the claimed planning-workflow omission is only partly generalizable because commercial tools already provide explicit pre-start course simulations and segment targets.","source_ids":["SRC1","SRC2","SRC3","SRC4"],"status":"PASS"}},"prior_art_disposition":"ESTABLISHED_PRACTICE","problem_evidence":{"finding":"The physical pacing problem is visible: terrain, wind, road conditions, fatigue, and environmental conditions change useful power allocation, and losses in slow technical sections may be unrecoverable. The workflow-gap claim is only partly supported because established products already predict course-specific consequences, optimize segment power, and support planned-versus-actual review.","source_ids":["SRC1","SRC2","SRC3","SRC4"],"status":"PARTLY_SUPPORTED"},"research_id":"eoa_inverse_innovation_exp13_light_screen_20260806","schema_version":1,"screen_id":"E13P148","screen_survival":false,"search_lanes":{"component_combination":{"no_result_note":null,"queries":["cycling time trial optimal pacing course topography wind power distribution simulation research","cycling pacing strategy critical power W prime course simulation optimization time trial","cycling time trial thermal strain pacing heat model pre race simulation power primary study","cycling time trial simulation software pacing plan forecast uncertainty product"],"source_ids":["SRC2","SRC3","SRC4"]},"direct_problem_and_intervention":{"no_result_note":null,"queries":["time trial cycling pre race course simulation pacing power plan wind elevation thermal strain uncertainty","cycling time trial pacing early effort cannot recover later performance study fast start"],"source_ids":["SRC1","SRC2","SRC3","SRC4"]},"products_practices_and_standards":{"no_result_note":null,"queries":["Best Bike Split race plan course weather power targets simulation official","site:bestbikesplit.com course simulation power pacing weather wind CdA race plan actual results","UCI regulations individual time trial rider power meter pacing radio official rules"],"source_ids":["SRC1","SRC2"]},"synonyms_and_historical_terms":{"no_result_note":null,"queries":["virtual elevation cycling pacing optimizer historical term optimal power distribution time trial","cycling time trial optimal pacing course topography wind power distribution simulation research","cycling pacing strategy critical power W prime course simulation optimization time trial"],"source_ids":["SRC3","SRC4"]}},"sources":[{"claims_supported":["Course- and weather-specific pre-start performance modeling is commercially available.","The product creates optimal power targets and precise race-day power instructions.","It supports scenario comparison and comparison of planned pacing with actual ride data."],"publisher":"Caffeinated Catalyst, LLC / Best Bike Split","source_id":"SRC1","source_type":"FIRST_PARTY_PRODUCT","title":"What is Best Bike Split and why would I use it?","url":"https://support.bestbikesplit.com/article/5/what-is-best-bike-split-and-why-would-i-use-it"},{"claims_supported":["A commercial system segments routes and optimizes power for each segment using rider, terrain, aerodynamic, weather, temperature, humidity, and road-surface inputs.","The system predicts race splits, runs what-if simulations, and exports segment power plans for execution.","Course-specific pre-start simulation and actionable power redistribution are established product functions."],"publisher":"Predictive Fitness, Inc.","source_id":"SRC2","source_type":"FIRST_PARTY_PRODUCT","title":"RaceX – Optimized Race Execution","url":"https://www.myracex.com/"},{"claims_supported":["Individualized fatigue and recovery can be incorporated into optimal-control pacing for hilly time trials.","Dynamic programming can produce course-specific power modes and schedules.","The study reports simulated improvement and a pilot experiment comparing near-optimal guidance with self-pacing."],"publisher":"arXiv","source_id":"SRC3","source_type":"PRIMARY_RESEARCH","title":"Optimal Pacing of a Cyclist in a Time Trial Based on Individualized Models of Fatigue and Recovery","url":"https://arxiv.org/abs/2007.11393"},{"claims_supported":["Dynamic optimization has modeled cycling time trials with slope, wind, corners, road friction, and critical-power constraints.","Course conditions can affect performance time and required peak power.","The simulation found that time lost in slow technical sections could not be regained on later fast sections."],"publisher":"SAGE Publications / Institution of Mechanical Engineers","source_id":"SRC4","source_type":"PRIMARY_RESEARCH","title":"Influence of corners and road conditions on cycling individual time trial performance and ‘optimal’ pacing strategy: A simulation study","url":"https://journals.sagepub.com/doi/10.1177/1754337120974872"}],"world_novelty_boundary":"This bounded public-web screen found established commercial and research practice covering most of the proposal's pacing-simulation structure. It does not establish world novelty, patentability, market size, expert acceptance, realized value, or the absence of undisclosed, proprietary, non-English, offline, or differently described systems."}