{"id":"W4410336261","doi":"10.1139/as-2024-0077","title":"Enhanced data collection in the Canadian Arctic for seabird bycatch information yields highly variable results","year":2025,"lang":"en","type":"article","venue":"Arctic Science","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Environment and Climate Change Canada","funders":"","keywords":"Seabird; Bycatch; Variable (mathematics); Arctic; Environmental science; Computer science; Fishery; Oceanography; Ecology; Biology; Mathematics; Geology; Fishing","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":[0.008299369,0.000802105,0.0004675782,0.005102717,0.002244906,0.001594091,0.001162022,0.0003352651,0.002904029],"category_scores_gemma":[0.01767061,0.0004522392,0.0006694921,0.008696593,0.0006073053,0.0008883736,0.001801643,0.0005113052,0.0006885165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007412926,"about_ca_system_score_gemma":0.01657992,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9099279,"about_ca_topic_score_gemma":0.9659244,"domain_scores_codex":[0.9908749,0.001928852,0.0007760864,0.001105739,0.004288162,0.001026261],"domain_scores_gemma":[0.9620798,0.00386785,0.005223015,0.002835701,0.02488669,0.001106937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002068382,0.000120177,0.9013748,0.0008379275,0.0003860326,0.0001067681,0.001907903,0.002063927,0.002958421,0.0007683017,0.01603149,0.0732374],"study_design_scores_gemma":[0.00001274779,0.00003551764,0.9781957,0.000157921,0.00005618215,0.00003185983,0.0009661323,0.001220221,0.001080888,0.0001244629,0.01808152,0.00003695593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.763869,0.002178812,0.02670534,0.001047268,0.0003878403,0.002322927,0.1594255,0.0007449597,0.04331837],"genre_scores_gemma":[0.8064048,0.001860052,0.0618788,0.0008415416,0.0001117775,0.00276044,0.1163327,0.0002126399,0.009597192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0900721,"threshold_uncertainty_score":0.181205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759841698497131,"score_gpt":0.2413576691844318,"score_spread":0.2237592521994605,"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."}}