{"id":"W7133287678","doi":"","title":"Newfoundland & Labrador comparative fishing analysis – part 1","year":2024,"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":"","keywords":"Fishing; Coast guard; Taxon; Submarine pipeline; Spring (device)","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":[0.0009815468,0.00046974,0.0003144598,0.004866944,0.00121623,0.001014338,0.0006201832,0.0001878522,0.01665172],"category_scores_gemma":[0.001233791,0.0002360924,0.0004618268,0.006604478,0.0005140969,0.0004691697,0.0008577817,0.0003427133,0.002162529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01094942,"about_ca_system_score_gemma":0.005055605,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8674753,"about_ca_topic_score_gemma":0.9632469,"domain_scores_codex":[0.9989391,0.00008604561,0.00006838703,0.0002330595,0.0003166207,0.000356791],"domain_scores_gemma":[0.9978577,0.0001327212,0.000408748,0.0001730456,0.001248248,0.00017942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003683635,0.0001546373,0.778219,0.000317538,0.0002579515,0.0007351825,0.001111631,0.001552619,0.004092803,0.001878352,0.06257519,0.1487367],"study_design_scores_gemma":[0.000004114858,0.00003793485,0.9759462,0.00002783871,0.00001476989,0.00006021512,0.000486077,0.000176226,0.0002373412,0.00002171079,0.02297936,0.000008277199],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7937263,0.001415393,0.002049037,0.0004157669,0.0000972499,0.0004306675,0.08754455,0.000213471,0.1141076],"genre_scores_gemma":[0.7632868,0.001239389,0.01057612,0.0006900782,0.00008551137,0.0006926292,0.09158218,0.0001603118,0.1316869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1325247,"threshold_uncertainty_score":0.2666103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606957889528008,"score_gpt":0.2637331768505065,"score_spread":0.2476635979552264,"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."}}