{"id":"W2953967416","doi":"10.1093/beheco/arz111","title":"Biological market effects predict cleaner fish strategic sophistication","year":2019,"lang":"en","type":"article","venue":"Behavioral Ecology","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Sophistication; Visitor pattern; Marketing; Population; Competition (biology); Reputation; Service (business); Order (exchange); Business; Biology; Ecology; Finance; Computer science; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004812877,0.00008141404,0.0001283837,0.00003755678,0.0002373998,0.00002287624,0.0001576552,0.0002303988,0.005301952],"category_scores_gemma":[0.0000554019,0.00007525292,0.00004033476,0.0001172097,0.0002357611,0.0001298885,0.00002718219,0.0001225881,0.0005208861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055688,"about_ca_system_score_gemma":0.000126316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009559993,"about_ca_topic_score_gemma":0.0006531097,"domain_scores_codex":[0.998691,0.0005791741,0.0001403228,0.000223537,0.0001108975,0.0002550308],"domain_scores_gemma":[0.9995128,0.0001717563,0.00005329068,0.0001263841,0.00006385679,0.00007189344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002783283,0.0008557222,0.3318666,0.00001921752,0.00001515991,0.00003111126,0.001202993,0.00004105005,0.003528701,0.6505449,0.006336671,0.005279531],"study_design_scores_gemma":[0.000762395,0.001515749,0.9524805,0.00001135144,0.00005183967,0.000007734862,0.001371131,0.0002436264,0.00008214615,0.02833925,0.01480007,0.0003342071],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9555169,0.00001322558,0.00001348918,0.0005722215,0.0006534678,0.0004094031,0.000007753813,0.00009978582,0.04271369],"genre_scores_gemma":[0.9947814,0.00002543332,0.00006921908,0.0002863445,0.0001716705,0.00005321591,0.00004673565,0.000005411972,0.004560608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6222057,"threshold_uncertainty_score":0.9956073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645551329612974,"score_gpt":0.3177247125424703,"score_spread":0.2812691992463405,"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."}}