{"id":"W7072236703","doi":"","title":"You can’t always get what you want: fish, sensors and fishermen","year":2019,"lang":"en","type":"article","venue":"CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach)","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l’Environnement, de la Protection de la nature et des Parcs; Queen's University","keywords":"Brown trout; Fishing; Trout; Catch and release; Stocking; Stock (firearms); Habitat; Recreation","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001132793,0.0003452183,0.0002477751,0.0004732305,0.004370639,0.002782175,0.0005827139,0.001360892,0.03436875],"category_scores_gemma":[0.002595196,0.0003041496,0.0001288365,0.0006062744,0.00170441,0.003081378,0.002777555,0.001746317,0.01201444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002074196,"about_ca_system_score_gemma":0.00172552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03322848,"about_ca_topic_score_gemma":0.06885459,"domain_scores_codex":[0.9990791,0.0002325936,0.00002285675,0.00008658173,0.0003260283,0.0002528469],"domain_scores_gemma":[0.9988983,0.0001077413,0.00007092684,0.00003959584,0.0002209783,0.0006623582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001021936,0.0001730724,0.03411043,0.0002177437,0.00001029212,0.001080877,0.03203048,0.00009810584,0.002741599,0.004779884,0.6835347,0.2411207],"study_design_scores_gemma":[0.000006762734,0.00009266913,0.01856088,0.0001965118,0.000005429016,0.00073913,0.06371225,0.0001003933,0.0002679653,0.001094869,0.9151853,0.00003783602],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1971416,0.01319079,0.004784397,0.3337675,0.007472446,0.0002836495,0.00115494,0.001210385,0.4409942],"genre_scores_gemma":[0.3739859,0.008388621,0.00647435,0.05383281,0.0007960639,0.0002159608,0.0006527661,0.0003260356,0.5553275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03436875,"threshold_uncertainty_score":0.1149749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02283574292453806,"score_gpt":0.2653545591124614,"score_spread":0.2425188161879233,"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."}}