{"judgments":[{"pair_id":"E13Q010","reason":"A converts predecessor boundedness into real isolation, capacity, and commissioning boundaries, adding a distinct and independently testable complement to P1; B is strong but largely repeats P1’s governance form.","scores_a":{"causal_coherence":5,"contrivance_risk":2,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":3,"structural_fidelity":5,"testability":5},"scores_b":{"causal_coherence":5,"contrivance_risk":1,"incremental_portfolio_value":3,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":5},"winner":"A"},{"pair_id":"E13Q040","reason":"B uses a directly measurable dew-point mechanism before an irreversible opening decision; its prediction, adjustment, and calibration chain is stronger and less model-dependent than A’s retrospective route forecast.","scores_a":{"causal_coherence":4,"contrivance_risk":3,"incremental_portfolio_value":4,"operational_specificity":5,"practicality":3,"structural_fidelity":5,"testability":4},"scores_b":{"causal_coherence":5,"contrivance_risk":1,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":5},"winner":"B"},{"pair_id":"E13Q005","reason":"Both are credible hybrid representations, but A more clearly connects the selected granularity to an allocation decision and supplies explicit boundary and cadence validation.","scores_a":{"causal_coherence":4,"contrivance_risk":2,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":4},"scores_b":{"causal_coherence":4,"contrivance_risk":2,"incremental_portfolio_value":4,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":4},"winner":"A"},{"pair_id":"E13Q033","reason":"B materially reinstates the predecessor’s limiting envelope and exposes cumulative interactions before commitment, giving the portfolio a more distinctive testable complement than A’s otherwise solid scope-governance translation.","scores_a":{"causal_coherence":5,"contrivance_risk":2,"incremental_portfolio_value":4,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":4},"scores_b":{"causal_coherence":5,"contrivance_risk":2,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":3,"structural_fidelity":5,"testability":5},"winner":"B"},{"pair_id":"E13Q053","reason":"B has a clear accumulated stock, quantified physical clearance lever, finite buffer, and explicit checks for displacement and rebound. A’s directional balance target is more contingent and easier to mistake for a substantive learning outcome.","scores_a":{"causal_coherence":4,"contrivance_risk":3,"incremental_portfolio_value":4,"operational_specificity":5,"practicality":4,"structural_fidelity":4,"testability":4},"scores_b":{"causal_coherence":5,"contrivance_risk":2,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":3,"structural_fidelity":5,"testability":4},"winner":"B"},{"pair_id":"E13Q030","reason":"A supplies a genuine pre-exposure consequence model with physically adjustable controls and calibration. B is well specified but its forecast of interpretive consequences is much less stable and more readily becomes formalized editorial preference.","scores_a":{"causal_coherence":4,"contrivance_risk":2,"incremental_portfolio_value":5,"operational_specificity":5,"practicality":3,"structural_fidelity":5,"testability":4},"scores_b":{"causal_coherence":3,"contrivance_risk":4,"incremental_portfolio_value":4,"operational_specificity":5,"practicality":4,"structural_fidelity":5,"testability":3},"winner":"A"}]}