{"id":"W4404943967","doi":"10.1101/2024.11.29.626127","title":"Enhanced data collection in the Canadian Arctic for seabird bycatch information yields highly variable results","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Environment and Climate Change Canada","funders":"","keywords":"Seabird; Bycatch; Variable (mathematics); Arctic; The arctic; Environmental science; Computer science; Geography; Fishery; Oceanography; Mathematics; Ecology; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01272361,0.0009303435,0.00048123,0.004355344,0.00225409,0.001617223,0.001064426,0.0003506356,0.002159507],"category_scores_gemma":[0.02009298,0.000477704,0.0008448291,0.006891893,0.0006806779,0.0007410138,0.001845041,0.0005508757,0.0006320158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006555359,"about_ca_system_score_gemma":0.01375001,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8234842,"about_ca_topic_score_gemma":0.9263755,"domain_scores_codex":[0.9834095,0.003277473,0.001424846,0.001889312,0.007989403,0.002009479],"domain_scores_gemma":[0.9400405,0.006019759,0.007350719,0.003662006,0.04105053,0.001876572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003396226,0.0001177605,0.9343152,0.0007289938,0.0003694306,0.0001115613,0.001577674,0.001114716,0.005218982,0.0002401073,0.007865798,0.04800018],"study_design_scores_gemma":[0.000008638687,0.00004789858,0.9880329,0.0001233566,0.00005419112,0.0000300234,0.00105779,0.0007105471,0.001829186,0.00003739043,0.008038389,0.00002953683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8981129,0.001438111,0.01552087,0.0007209911,0.0003319209,0.001826678,0.05689485,0.0005243096,0.02462939],"genre_scores_gemma":[0.9052521,0.001033841,0.0365734,0.0006983583,0.00008802414,0.002230551,0.04772686,0.0001536333,0.006243145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1765158,"threshold_uncertainty_score":0.3551106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707817118351639,"score_gpt":0.2103068835461402,"score_spread":0.1932287123626238,"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."}}