{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005558949,0.0002293182,0.0002693317,0.001115893,0.0003674494,0.0005397815,0.0003176781,0.0005018275,0.003559524],"category_scores_gemma":[0.002462642,0.0002483882,0.0003771114,0.0004542141,0.0006822382,0.0004938681,0.0006543501,0.0004398224,0.0002301268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005153565,"about_ca_system_score_gemma":0.0001585868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003357435,"about_ca_topic_score_gemma":0.009546965,"domain_scores_codex":[0.9997019,0.00006003758,0.00001808548,0.0001273048,0.00005168328,0.0000408394],"domain_scores_gemma":[0.9972258,0.000853618,0.001200455,0.0001955261,0.0001578079,0.0003668745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001406214,0.0001030829,0.9722611,0.00002677068,0.000111253,0.00009672483,0.0002514245,0.00139868,0.02315792,0.0001814539,0.0000624705,0.002208516],"study_design_scores_gemma":[0.000001547833,0.00006420971,0.9966085,0.000001210885,0.00001257284,0.00003734139,0.0001021611,0.002445111,0.0005511757,0.0001264168,0.00004557539,0.000004206403],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994672,0.00001705554,0.0001929347,0.000009500718,5.948037e-7,0.000002873861,0.00008070344,0.000003624937,0.0002255515],"genre_scores_gemma":[0.9996467,0.000005158142,0.0001735117,0.00000477678,8.532426e-7,0.000004025783,0.00004825752,0.000001578692,0.0001152815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003559524,"threshold_uncertainty_score":0.01190782,"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."}}