{"id":"W7133271536","doi":"","title":"Assessment of 4VWX herring","year":2022,"lang":"en","type":"other","venue":"Federal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fisheries and Oceans Canada","keywords":"Stock (firearms); Herring; Fishing; Fish stock; Bay; Fisheries management","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":[0.0009146499,0.0004011061,0.0001467242,0.001378927,0.0006254266,0.001042769,0.0003724278,0.0003022896,0.002372775],"category_scores_gemma":[0.0006246539,0.0001447345,0.0002667496,0.0005192759,0.0002407464,0.0002394637,0.0004315451,0.0002345295,0.0006477251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003744526,"about_ca_system_score_gemma":0.001502042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4197877,"about_ca_topic_score_gemma":0.7351682,"domain_scores_codex":[0.9993309,0.00003289019,0.00002322347,0.00007626834,0.0004409119,0.0000958134],"domain_scores_gemma":[0.9994494,0.00002360549,0.0000709877,0.00001586585,0.0003565766,0.0000834509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001639803,0.00006513717,0.9210465,0.00007808384,0.00008339565,0.0005107066,0.0004920658,0.003276621,0.0125021,0.0004546885,0.002637166,0.05868961],"study_design_scores_gemma":[0.000003881756,0.0001672301,0.9902146,0.00004384573,0.00002433452,0.0001038433,0.0008008222,0.002731402,0.001161168,0.00008247263,0.004656948,0.000009484185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758424,0.0005058224,0.0009656942,0.00009757286,0.00002754222,0.00008968249,0.00185193,0.00003180153,0.02058757],"genre_scores_gemma":[0.980587,0.0003375305,0.00130766,0.00007621713,0.000005848428,0.00002811776,0.002042642,0.000008491123,0.01560645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5802124,"threshold_uncertainty_score":0.8346885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009648494635959183,"score_gpt":0.2562425179460947,"score_spread":0.2465940233101355,"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."}}