{"id":"W4409838805","doi":"10.1101/2025.04.23.650286","title":"Estimating dead fish quantities dropping out of gillnets when direct observations are impossible","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada","keywords":"Fish <Actinopterygii>; Fishery; Mathematics; Statistics; Econometrics; Environmental science; Mathematical economics; Biology","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":[0.001051244,0.0007292174,0.000300078,0.001768465,0.0003095156,0.0008467981,0.0007804749,0.0003648373,0.001232295],"category_scores_gemma":[0.00352736,0.0003479825,0.0007329496,0.001094174,0.0004316626,0.0007073834,0.0007941308,0.0003005688,0.0004131906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00138058,"about_ca_system_score_gemma":0.0006907896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05045921,"about_ca_topic_score_gemma":0.09366722,"domain_scores_codex":[0.9993352,0.0001074718,0.00008739295,0.0002260135,0.0001703766,0.00007345173],"domain_scores_gemma":[0.9979563,0.0005535726,0.0009264101,0.0001918949,0.0003026031,0.00006915501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008489328,0.00003544739,0.9502604,0.00008672218,0.000171437,0.00006775561,0.0001424553,0.02956403,0.003608451,0.0002964635,0.0003282247,0.01535369],"study_design_scores_gemma":[0.00000867477,0.0001230215,0.8561538,0.00006038656,0.00009506484,0.0002057264,0.0003494608,0.1348832,0.00571545,0.0009094199,0.001454835,0.00004085959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507344,0.0003816911,0.04369222,0.00006451993,0.00001434814,0.00008527708,0.002884576,0.0002135933,0.001929332],"genre_scores_gemma":[0.9703821,0.0001697703,0.02488541,0.00002399918,0.000006915955,0.00006553833,0.003107337,0.00003073572,0.001328241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05045921,"threshold_uncertainty_score":0.100331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03858028032857706,"score_gpt":0.2650734309454016,"score_spread":0.2264931506168245,"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."}}