{"id":"W7104656050","doi":"10.5061/dryad.zs7h44j9b","title":"Data from: Avoiding dead ends: the experimental evolution of constraint as adaptation to environmental variation","year":2022,"lang":"en","type":"dataset","venue":"DRYAD","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":[0.002556563,0.002654228,0.002028694,0.00276334,0.001554824,0.002849717,0.00361905,0.004159035,0.03256519],"category_scores_gemma":[0.01081559,0.0008699669,0.001872486,0.004412233,0.0009130338,0.001258469,0.00242953,0.002382702,0.05082785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344294,"about_ca_system_score_gemma":0.001767686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01299101,"about_ca_topic_score_gemma":0.02963956,"domain_scores_codex":[0.9980191,0.0004508561,0.0002236818,0.0006383362,0.000467997,0.0002000223],"domain_scores_gemma":[0.9943468,0.002603546,0.0005825519,0.00143177,0.0007492317,0.00028604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004979282,0.0001800854,0.007721134,0.00351003,0.0002940324,0.0001286683,0.0001180487,0.001905057,0.001192534,0.001005927,0.9775949,0.005851617],"study_design_scores_gemma":[0.001092743,0.0001244145,0.01988319,0.0006084927,0.0001985629,0.0001852187,0.0001561567,0.001736599,0.002096176,0.002796448,0.9710075,0.0001144664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009205746,0.0002486954,0.0001421062,0.0001069045,0.00003827765,0.0000157583,0.9976648,0.0003963974,0.0004665143],"genre_scores_gemma":[0.001266524,0.00007141892,0.0004681429,0.00007418966,0.000006788825,0.0001150595,0.9975023,0.00007085265,0.000424704],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03256519,"threshold_uncertainty_score":0.1089414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05368996535389194,"score_gpt":0.295989902819634,"score_spread":0.2422999374657421,"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."}}