{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540452,0.0001267142,0.000157752,0.0006375071,0.000782042,0.0005410064,0.0003139332,0.0002221425,0.001786028],"category_scores_gemma":[0.0006823475,0.0002337312,0.0001882266,0.000708284,0.0005878987,0.000226318,0.0005467489,0.0002054676,0.0002660552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004283361,"about_ca_system_score_gemma":0.001359325,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6670951,"about_ca_topic_score_gemma":0.9214515,"domain_scores_codex":[0.9998975,0.00001353987,0.000005128306,0.00001972888,0.00002551634,0.0000386656],"domain_scores_gemma":[0.9993458,0.00006286363,0.0001582595,0.00001979625,0.0001067968,0.0003065511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001909994,0.00001124282,0.9939197,0.000009717434,0.00003270443,0.000110698,0.001795173,0.0001755491,0.001999815,0.00006084679,0.0002801323,0.001413496],"study_design_scores_gemma":[5.147857e-7,0.000006330454,0.9995615,7.663178e-7,0.000001493219,0.000009949567,0.0003030375,0.00003037494,0.00002009091,0.000002833656,0.00006188945,0.000001319248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993901,0.00003876809,0.0000114544,0.00002594588,0.000001664045,9.577412e-7,0.0001592828,0.000001563047,0.0003703166],"genre_scores_gemma":[0.9991927,0.00003806348,0.0000150738,0.000006060452,0.000001696294,0.000001186621,0.000122763,0.000001496151,0.0006210142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3329049,"threshold_uncertainty_score":0.6697309,"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."}}