{"id":"W2099715958","doi":"10.1093/icesjms/fsp220","title":"Hierarchical analysis of a remote, Arctic, artisanal longline fishery","year":2009,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Bedford Institute of Oceanography; University of Windsor","funders":"National Oceanic and Atmospheric Administration; Government of Canada","keywords":"Bycatch; Catch per unit effort; Fishery; Fishing; Stock assessment; Stock (firearms); Geography; Generalized linear model; Halibut; Generalized additive model; Groundfish; Environmental science; Oceanography; Fisheries management; Statistics; Fish <Actinopterygii>; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001201277,0.0002972718,0.0003474881,0.001354415,0.000511321,0.0005177507,0.0004104293,0.0001963175,0.001466158],"category_scores_gemma":[0.002976127,0.0001517549,0.001010944,0.0009998712,0.0003298192,0.0004092588,0.0007384222,0.0003946106,0.0002070815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001222545,"about_ca_system_score_gemma":0.0007840695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07606,"about_ca_topic_score_gemma":0.1280687,"domain_scores_codex":[0.9994546,0.0001896186,0.00002516185,0.0001591234,0.00009203287,0.00007954458],"domain_scores_gemma":[0.998694,0.0005133591,0.0002486359,0.0001443938,0.0002719623,0.0001278036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002897472,0.0001644216,0.8760267,0.0001320889,0.0007717073,0.0004292768,0.001278447,0.04781793,0.01596283,0.002308266,0.001065144,0.05375343],"study_design_scores_gemma":[0.000009800615,0.0001614957,0.8515862,0.00002563038,0.0001001121,0.0000482135,0.0005125252,0.1435839,0.0007048402,0.002479197,0.0007529629,0.00003517224],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879624,0.0001452791,0.01040544,0.0000579908,0.000004456976,0.00002779806,0.0005859763,0.0001003301,0.0007102868],"genre_scores_gemma":[0.9937763,0.00003111286,0.00508055,0.00001928565,0.0000031585,0.00001404796,0.0007136689,0.00001386199,0.0003478987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07606,"threshold_uncertainty_score":0.1512346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365933802566666,"score_gpt":0.2769841769654158,"score_spread":0.2633248389397492,"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."}}