{"id":"W7133268421","doi":"","title":"Northern Contingent Atlantic Mackerel Stock Assessment in 2024","year":2025,"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":"Stock (firearms); Fishing; Fish stock; Stock assessment; Climate change; Horse mackerel","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":[0.0008881768,0.000238486,0.0001321788,0.0008247505,0.0003304053,0.0006437362,0.0004564614,0.000286736,0.006225598],"category_scores_gemma":[0.001146819,0.0001513969,0.0004902236,0.0003836755,0.0001057793,0.0004396183,0.0006436073,0.0002318681,0.001082473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002331473,"about_ca_system_score_gemma":0.001423437,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2119108,"about_ca_topic_score_gemma":0.4131282,"domain_scores_codex":[0.9997733,0.00003061922,0.00001549802,0.00002077946,0.000114243,0.00004550753],"domain_scores_gemma":[0.9995215,0.00003631652,0.00007338509,0.00001978464,0.0002845555,0.00006453247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003243599,0.0001001214,0.8675411,0.00006995808,0.0002134054,0.0007244046,0.0004108793,0.0393945,0.00147641,0.005065592,0.02314515,0.06153405],"study_design_scores_gemma":[0.00004396433,0.0002829714,0.7867318,0.000150098,0.0001511037,0.0002534936,0.00167267,0.1286281,0.001981606,0.002878745,0.0771502,0.0000752653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9132428,0.0001451687,0.002429921,0.0006636332,0.00004446012,0.0000984485,0.01664304,0.0001162297,0.06661642],"genre_scores_gemma":[0.9430327,0.0001556857,0.003237944,0.0001155465,0.00001116404,0.00008244249,0.01553147,0.00002028124,0.0378128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7880892,"threshold_uncertainty_score":0.4213548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008254605466557806,"score_gpt":0.2533629235990524,"score_spread":0.2451083181324946,"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."}}