{"id":"W4406062098","doi":"10.1016/j.applanim.2024.106501","title":"Prediction of successful training outcomes for drug detection dogs using subjective ratings and behavioral test measures: A case study in Japan customs","year":2025,"lang":"en","type":"article","venue":"Applied Animal Behaviour Science","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Test (biology); Psychology; Animal-assisted therapy; HUBzero; Clinical psychology; Training (meteorology); Applied psychology; Pet therapy; Animal welfare; Ecology; Biology; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006176514,0.0001615092,0.0002222057,0.000229286,0.0003621145,0.00005907977,0.0001302519,0.0000580404,8.172667e-7],"category_scores_gemma":[0.0001309458,0.0001576265,0.0000433855,0.0003736002,0.0003235374,0.00003026309,0.0001389246,0.0001155129,1.223327e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006263651,"about_ca_system_score_gemma":0.0001144808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001281936,"about_ca_topic_score_gemma":0.004682668,"domain_scores_codex":[0.998693,0.00002293468,0.0003327754,0.0005285878,0.0001858347,0.0002368171],"domain_scores_gemma":[0.9994069,0.00004951905,0.0001429987,0.0001436005,0.0002134248,0.00004355782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005359183,0.0001479247,0.5458242,0.000004606758,0.000006509055,0.000003342852,0.001903997,0.000004563441,0.4516222,0.000009337291,6.998008e-7,0.000419106],"study_design_scores_gemma":[0.0009979503,0.0007711119,0.7577817,0.00001821207,0.00009508588,0.00006366946,0.0526519,0.0002285659,0.1872287,0.000008058558,0.000002237755,0.0001528362],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986607,0.00001981429,0.0003601218,0.000007179441,0.00008465718,0.0007684247,0.0000220295,0.0000156796,0.00006132905],"genre_scores_gemma":[0.9994476,0.00000140339,0.0003294219,0.00001383631,0.00001943563,0.0001566339,0.000001841835,0.00001009101,0.0000196723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2643935,"threshold_uncertainty_score":0.6427821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05499429039746245,"score_gpt":0.3755489899789398,"score_spread":0.3205546995814773,"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."}}