{"id":"W2162412071","doi":"10.1093/beheco/ars085","title":"Exposing the behavioral gambit: the evolution of learning and decision rules","year":2012,"lang":"en","type":"article","venue":"Behavioral Ecology","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":294,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Gambit; Mechanism (biology); Biology; Selection (genetic algorithm); Cognitive psychology; Cognition; Stability (learning theory); Artificial intelligence; Cognitive science; Computer science; Machine learning; Psychology; Neuroscience; Simulation","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":[0.002172647,0.0004426884,0.0003796704,0.0006465489,0.0007871227,0.003392267,0.0008563118,0.002144622,0.004360142],"category_scores_gemma":[0.008561552,0.0003751275,0.0005753911,0.0004384216,0.007185303,0.00517323,0.001678664,0.002921738,0.0004713161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008910686,"about_ca_system_score_gemma":0.0004829788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547016,"about_ca_topic_score_gemma":0.0008703135,"domain_scores_codex":[0.9988959,0.0006150841,0.00002672392,0.0002318908,0.0001575535,0.00007286559],"domain_scores_gemma":[0.9971663,0.001774079,0.0003045566,0.0003819529,0.0001954305,0.0001776914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000113336,0.00008332458,0.01245261,0.0001404605,0.0001188171,0.0002422846,0.001481673,0.02214998,0.004295519,0.8947459,0.002793868,0.06138215],"study_design_scores_gemma":[0.00001453256,0.00003906887,0.003886318,0.00004920728,0.00002664164,0.0002255883,0.0002124676,0.02468303,0.001113953,0.9615999,0.008106008,0.00004323437],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3824297,0.007915846,0.4918283,0.04181186,0.0004846912,0.00004855052,0.0003948936,0.0005476243,0.07453867],"genre_scores_gemma":[0.9192457,0.001992552,0.06972448,0.001758144,0.000128343,0.00006319799,0.0001167633,0.0001161809,0.006854577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004360142,"threshold_uncertainty_score":0.01458609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03879358792628718,"score_gpt":0.2943574302878783,"score_spread":0.2555638423615911,"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."}}