{"id":"W2767400871","doi":"10.1080/03632415.2017.1377558","title":"Fish Bioenergetics 4.0: An R-Based Modeling Application","year":2017,"lang":"en","type":"article","venue":"Fisheries","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":208,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"U.S. Geological Survey; U.S. Fish and Wildlife Service; Ipsen; South Dakota State University","keywords":"Bioenergetics; Fish <Actinopterygii>; Computer science; Ecology; Range (aeronautics); Environmental science; Biology; Fishery; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000088714,0.00007319327,0.00007039042,0.000008321083,0.0007785068,0.00006378365,0.0002926458,0.00004086016,0.0004890825],"category_scores_gemma":[0.00003609005,0.00007108487,0.00001775667,0.00001924222,0.00023111,0.0003476084,0.000183462,0.00004085007,0.00009988286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002303547,"about_ca_system_score_gemma":0.000001928101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002389058,"about_ca_topic_score_gemma":0.005736502,"domain_scores_codex":[0.999498,0.00001127285,0.00007813211,0.0001900419,0.00008196611,0.0001406392],"domain_scores_gemma":[0.9994836,0.000006582716,0.00005514217,0.0004196817,0.000004893278,0.00003005967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002188031,0.00009996806,0.9322899,0.00001180296,0.00001388582,0.000002969509,0.0002405872,0.01240489,0.0001727538,0.0003753502,0.04949503,0.004870966],"study_design_scores_gemma":[0.0002926299,0.00008690691,0.6762663,0.000003617923,0.00002342861,2.935111e-7,0.0001292206,0.2169365,0.0003696275,0.003051643,0.1025816,0.0002582965],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9046422,0.000001948986,0.01470796,0.007883491,0.0001839865,0.0001706398,0.00000602641,0.0001113522,0.07229243],"genre_scores_gemma":[0.9961492,0.000009172646,0.001722183,0.001312947,0.00003286751,0.00005130075,0.00001349701,0.000007352228,0.0007014923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2560237,"threshold_uncertainty_score":0.5987723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241676367319942,"score_gpt":0.2389988206865807,"score_spread":0.2165820570133812,"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."}}