{"id":"W3180944725","doi":"","title":"Investigating the drivers of Atlantic salmon populations decline at the North Atlantic basin scale through a Bayesian life cycle modelling approach","year":2019,"lang":"en","type":"preprint","venue":"Prodinra (INRA Bordeaux-Aquitaine)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Scale (ratio); Structural basin; Atlantic hurricane; Bayesian probability; Oceanography; Fishery; Environmental science; Geography; Computer science; Geology; Artificial intelligence; Cartography; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001318896,0.0006920887,0.000828274,0.00009450489,0.0008869743,0.0002387485,0.002104968,0.0003579231,0.0009824125],"category_scores_gemma":[0.000386038,0.0004621813,0.0003889155,0.0008670314,0.00123572,0.0003369024,0.007205406,0.001512406,0.000136409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648515,"about_ca_system_score_gemma":0.0002075445,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02375411,"about_ca_topic_score_gemma":0.004847943,"domain_scores_codex":[0.9943717,0.0004896356,0.001123571,0.001336766,0.001618063,0.001060266],"domain_scores_gemma":[0.9964836,0.0003144189,0.0007252733,0.002109679,0.0001022861,0.0002647988],"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":[0.00003215897,0.0001202741,0.6467937,0.0002619306,0.00005573517,0.00000250645,0.002062963,0.3485214,0.00002643516,0.0002566388,0.001308611,0.0005576111],"study_design_scores_gemma":[0.0005855902,0.0001106016,0.06600524,0.0001326839,0.0002282137,0.00001676188,0.0008131743,0.9190084,0.00002569134,0.004076042,0.008150528,0.0008470173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283062,0.0001162416,0.02621664,0.004748353,0.0003197464,0.003290781,0.00006824492,0.000118252,0.03681558],"genre_scores_gemma":[0.9850911,0.0002984264,0.01126529,0.0005169968,0.0002486367,0.0002108157,0.0006410725,0.0001123728,0.00161529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5807885,"threshold_uncertainty_score":0.9999308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413654338332997,"score_gpt":0.267110529094666,"score_spread":0.2257450952613663,"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."}}