{"id":"W3168132747","doi":"10.1139/cjfas-2020-0267","title":"Spatiotemporal modeling of bycatch data: methods and a practical guide through a case study in a Canadian Arctic fishery","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Dalhousie University","funders":"","keywords":"Bycatch; Fishery; Fishing; Computer science; Limiting; Arctic; Fisheries management; Ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01704171,0.001054884,0.0004753261,0.002222414,0.002368881,0.002808028,0.002541162,0.001397398,0.001436319],"category_scores_gemma":[0.02173859,0.0005582085,0.001200032,0.004552834,0.001600837,0.001754513,0.002612062,0.00150276,0.0002246472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008795473,"about_ca_system_score_gemma":0.00930384,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.576283,"about_ca_topic_score_gemma":0.7250826,"domain_scores_codex":[0.9928012,0.004882908,0.0004989011,0.0005139742,0.0009897508,0.0003133327],"domain_scores_gemma":[0.9801738,0.01482517,0.001006674,0.001063797,0.002597345,0.0003332004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003259518,0.0007504204,0.2336655,0.00204679,0.0004087517,0.006153212,0.02815255,0.3126102,0.004576302,0.1089858,0.02252683,0.2797976],"study_design_scores_gemma":[0.0001107039,0.0004376126,0.08105804,0.001973067,0.0002654741,0.00166202,0.04258379,0.7003793,0.004348218,0.04464626,0.1220129,0.0005227507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2404302,0.002754137,0.7246407,0.009919153,0.0001294448,0.0027412,0.005825339,0.0005091008,0.01305081],"genre_scores_gemma":[0.3282841,0.003209631,0.6609674,0.0004510701,0.00004790038,0.00164502,0.002069133,0.000184372,0.003141301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.423717,"threshold_uncertainty_score":0.8524247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1362833233407902,"score_gpt":0.3930305253264219,"score_spread":0.2567472019856317,"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."}}