{"id":"W2177142660","doi":"10.1139/cjfas-2015-0163","title":"Retrospective forecasting — evaluating performance of stock projections for New England groundfish stocks","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Northeast Fisheries Science Center; National Marine Fisheries Service; National Oceanic and Atmospheric Administration","keywords":"Groundfish; Stock assessment; Stock (firearms); Econometrics; Population; Statistics; Computer science; Environmental science; Fishery; Geography; Economics; Fisheries management; Mathematics; Demography; Fishing; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01672245,0.0008078734,0.0004624157,0.0009660549,0.0003512296,0.001180831,0.0007922329,0.0006552197,0.0008356809],"category_scores_gemma":[0.04557021,0.0005563001,0.000759334,0.0009573118,0.000341526,0.001676933,0.0008493359,0.0006938392,0.0001607413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188992,"about_ca_system_score_gemma":0.001046436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03004896,"about_ca_topic_score_gemma":0.01644365,"domain_scores_codex":[0.9970145,0.001812862,0.0002996221,0.0004311637,0.0003496355,0.00009213886],"domain_scores_gemma":[0.9569864,0.03214536,0.003420482,0.002816243,0.004174698,0.0004567316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009536148,0.00008927657,0.1280166,0.00009340797,0.0003398902,0.00008169595,0.0002486633,0.8361492,0.0006545738,0.001335851,0.0007109415,0.03132626],"study_design_scores_gemma":[0.00009466681,0.0007509483,0.04402234,0.00006338843,0.0001151718,0.00006169535,0.0001899354,0.951165,0.001445735,0.001027946,0.001003653,0.00005951454],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755072,0.0003117062,0.01953387,0.0002211992,0.00004216735,0.0001423206,0.001715854,0.0003122271,0.002213459],"genre_scores_gemma":[0.9841196,0.0001212193,0.01355278,0.00002190709,0.00001231136,0.00006558131,0.001835716,0.00002569227,0.0002451402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03004896,"threshold_uncertainty_score":0.08843786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062425850550563,"score_gpt":0.294747487620681,"score_spread":0.1885049025656247,"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."}}