{"id":"W2705885435","doi":"","title":"Annual Depth and Temperature Selection by Fishes in Toronto Harbour","year":2012,"lang":"en","type":"article","venue":"AFS 142nd Annual Meeting","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Harbour; Selection (genetic algorithm); Geography; Fishery; Biology; Computer science","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":[0.0003411337,0.000119871,0.0001169237,0.00001375554,0.0002125452,0.0000120178,0.00007120596,0.00008160649,0.0002875275],"category_scores_gemma":[0.00007677307,0.000111184,0.00001479645,0.00009335402,0.00007011264,0.0008277961,0.0001856987,0.0001119204,0.00003656628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360359,"about_ca_system_score_gemma":0.000001618085,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001836628,"about_ca_topic_score_gemma":0.02633532,"domain_scores_codex":[0.9991257,0.00006021498,0.0001267358,0.0002131758,0.0001023629,0.0003717979],"domain_scores_gemma":[0.9997913,0.00004560402,0.00004341779,0.00005498007,0.000005406448,0.00005930456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008235352,0.00005869503,0.8638676,0.000008095434,0.00000997705,0.000001025213,0.002344796,0.000009254315,0.0008136395,0.00002814122,0.1318109,0.00103958],"study_design_scores_gemma":[0.0001887652,0.00005356955,0.9681994,0.00001198864,0.000009842134,0.000002859729,0.006061004,0.00001814938,0.0002592675,0.00002442606,0.02501889,0.00015179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663329,0.0004526721,0.000002288262,0.0005315581,0.0001398439,0.0001275971,0.00001580026,0.00004038378,0.03235701],"genre_scores_gemma":[0.9971586,0.0001063491,0.0002187915,0.0006783567,0.00008537876,0.00002166696,0.000004086932,0.000008896818,0.001717896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.106792,"threshold_uncertainty_score":0.9914315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003831754977449246,"score_gpt":0.2141549297301562,"score_spread":0.210323174752707,"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."}}