{"id":"W2363261994","doi":"10.1093/icesjms/fsw071","title":"Food for thought: pretty good multispecies yield","year":2016,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of New Brunswick","funders":"","keywords":"Yield (engineering); Environmental science; Materials science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001382197,0.0001042541,0.0001567307,0.00009145684,0.0002082255,0.0001002136,0.001090053,0.0000320684,0.004237473],"category_scores_gemma":[0.0008915843,0.00005906846,0.00008971683,0.0004276204,0.0008834323,0.001252159,0.0009988692,0.0001130261,0.00003247918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190626,"about_ca_system_score_gemma":0.00006620208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004224727,"about_ca_topic_score_gemma":0.000139104,"domain_scores_codex":[0.9981539,0.00002109154,0.0003134596,0.000228846,0.0008681917,0.0004145623],"domain_scores_gemma":[0.9989461,0.0002859554,0.0002281005,0.0002360519,0.00009641844,0.0002074095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001698642,0.0001009137,0.3635228,0.0000134165,0.00001350637,0.000009127711,0.0001358039,0.00000821,0.04394391,0.001105825,0.00211837,0.5888582],"study_design_scores_gemma":[0.001588677,0.003786116,0.3452142,0.00005239258,0.00002229032,0.0001871228,0.0001990464,0.0002453138,0.03635119,0.006133654,0.6057903,0.0004297853],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6028975,0.00001843746,0.004112761,0.003871614,0.0005831938,0.000332232,0.00001150884,0.00001918725,0.3881536],"genre_scores_gemma":[0.9817629,0.00009182357,0.01040553,0.0001106493,0.0001995359,0.000006546142,1.241885e-7,0.000008682312,0.007414177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6036718,"threshold_uncertainty_score":0.9966728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902726317043929,"score_gpt":0.2736145902428521,"score_spread":0.2445873270724128,"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."}}