{"id":"W2096205555","doi":"10.1093/icesjms/fsu055","title":"Time-varying natural mortality in fisheries stock assessment models: identifying a default approach","year":2014,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Stock assessment; Fishing; Stock (firearms); Econometrics; Population; Statistics; Fishery; Monte Carlo method; Environmental science; Mathematics; Geography; Biology; Demography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003863209,0.0001551027,0.0002768582,0.0001892709,0.0002409953,0.0002943916,0.001249263,0.00003885305,0.001115456],"category_scores_gemma":[0.0002243718,0.0001239322,0.00007895912,0.0009798869,0.0008736702,0.002860327,0.001742192,0.0005417002,0.00001185839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002917527,"about_ca_system_score_gemma":0.00009814012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009832846,"about_ca_topic_score_gemma":0.0001652726,"domain_scores_codex":[0.9968665,0.0001356842,0.0005188089,0.0003488018,0.001627801,0.0005023996],"domain_scores_gemma":[0.9990796,0.00008891529,0.0003025026,0.0002618083,0.00007516128,0.0001919782],"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":[0.00005288273,0.0002043601,0.8643675,0.00004453332,0.00001424843,0.00003120755,0.0006457721,0.0115397,0.006333993,0.0001424334,0.000181908,0.1164415],"study_design_scores_gemma":[0.0004628164,0.0001321277,0.4733838,0.00001735527,0.000009692463,0.0000800008,0.0001797834,0.5216992,0.0001357751,0.002669212,0.001012204,0.0002180092],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7209949,0.000006761805,0.002213589,0.0001208561,0.0001174333,0.0001081067,4.122195e-7,0.000009455519,0.2764284],"genre_scores_gemma":[0.9821151,0.00002158616,0.01699597,0.0000812707,0.00006003832,0.000005015798,0.000001006532,0.000008827514,0.0007111161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5101595,"threshold_uncertainty_score":0.9997976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04782299181237362,"score_gpt":0.3148087264294838,"score_spread":0.2669857346171102,"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."}}