{"id":"W4382058111","doi":"10.1111/fme.12639","title":"Using multivariate autoregressive state‐space models to examine stock structure of Greenland halibut in the North Atlantic","year":2023,"lang":"en","type":"article","venue":"Fisheries Management and Ecology","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Halibut; Multivariate statistics; Autoregressive model; Fishery; Stock (firearms); Geography; State-space representation; Econometrics; Oceanography; Statistics; Biology; Mathematics; Geology; Fish <Actinopterygii>","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":[],"consensus_categories":[],"category_scores_codex":[0.0001554199,0.0001233181,0.0001811711,0.00009114621,0.0001031283,0.00003419527,0.000264052,0.00004176959,0.0006740706],"category_scores_gemma":[0.00001495004,0.000089751,0.00001812641,0.0004451537,0.0001502268,0.0001647994,0.0007627446,0.0001039989,0.000009714795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003882504,"about_ca_system_score_gemma":0.000004223079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0030885,"about_ca_topic_score_gemma":0.02051829,"domain_scores_codex":[0.9989112,0.0000924834,0.000174416,0.0002659372,0.0001982168,0.0003577215],"domain_scores_gemma":[0.9996125,0.00006576172,0.00005553452,0.000212313,0.000006986084,0.00004696661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006373737,0.00002166057,0.985482,0.00005011492,0.00002415011,0.00008132509,0.002647408,0.00582597,0.0000549701,0.00009862339,0.00298007,0.002669957],"study_design_scores_gemma":[0.000333905,0.0001242052,0.9658701,0.000004033263,0.00001034352,0.000002721724,0.0006209956,0.02385068,0.00000465255,0.0009477999,0.008117087,0.0001134839],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921715,0.000001735646,0.0001995886,0.000578664,0.0000565067,0.00050051,0.00001354561,0.00001660569,0.006461383],"genre_scores_gemma":[0.9960757,0.00003969578,0.0005450643,0.0001296806,0.00001334092,0.00002416161,0.00002868468,0.00001066097,0.003133005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01961192,"threshold_uncertainty_score":0.9973547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04082619518046118,"score_gpt":0.2571011722937076,"score_spread":0.2162749771132464,"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."}}