{"id":"W7104650768","doi":"10.5281/zenodo.7060143","title":"Data from: Avoiding dead ends: the experimental evolution of constraint as adaptation to environmental variation","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Replicate; Constraint (computer-aided design); Selection (genetic algorithm); Experimental evolution; Trait; Adaptation (eye); Measure (data warehouse); Shock (circulatory)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003300955,0.001535971,0.001253349,0.001860257,0.001405896,0.002035816,0.002150012,0.002380986,0.04301844],"category_scores_gemma":[0.01385994,0.0005547831,0.001181625,0.002599367,0.000962566,0.001230179,0.00222027,0.002086691,0.0461475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475658,"about_ca_system_score_gemma":0.001207587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006942838,"about_ca_topic_score_gemma":0.0202338,"domain_scores_codex":[0.9978405,0.0004552984,0.0002184086,0.0006604543,0.0006420586,0.0001831751],"domain_scores_gemma":[0.9874561,0.005523575,0.0009622731,0.003958608,0.001534498,0.0005648198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001226458,0.0003473551,0.02301896,0.002128005,0.000274334,0.0001907847,0.0002980376,0.002860243,0.003726295,0.002334712,0.9463506,0.01724417],"study_design_scores_gemma":[0.0007461269,0.00023725,0.041614,0.0003493303,0.0001433748,0.0002305706,0.0002393869,0.002930223,0.007796204,0.004944497,0.940623,0.0001460666],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006894859,0.0002221917,0.001119067,0.0003292988,0.0001479055,0.0000521594,0.9861146,0.001590993,0.003528831],"genre_scores_gemma":[0.01002842,0.0001059029,0.003347288,0.0002552268,0.00002976133,0.0003347281,0.9824027,0.0006482514,0.002847787],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04301844,"threshold_uncertainty_score":0.143911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07199169683199431,"score_gpt":0.3312462380353343,"score_spread":0.25925454120334,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}