{"id":"W3159482275","doi":"10.1111/faf.12562","title":"Forecasting fish recruitment in age‐structured population models","year":2021,"lang":"en","type":"article","venue":"Fish and Fisheries","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Stock assessment; Stock (firearms); Population; Econometrics; Computer science; Fish stock; Autocorrelation; Operations research; Statistics; Fishery; Fish <Actinopterygii>; Economics; Geography; Engineering; Mathematics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001158638,0.000118513,0.000159655,0.00002441992,0.0001166985,0.0001252421,0.00008964669,0.00007879073,0.003535173],"category_scores_gemma":[0.00007891368,0.0001170942,0.00003000569,0.000241927,0.00009603184,0.0004497507,0.0002887683,0.0001535177,0.00000246543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007012044,"about_ca_system_score_gemma":0.000009363067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071543,"about_ca_topic_score_gemma":0.01236693,"domain_scores_codex":[0.9989201,0.0000524057,0.0001993718,0.0003128647,0.000220576,0.0002946464],"domain_scores_gemma":[0.9996667,0.00003388111,0.00003456703,0.000169013,0.000009225159,0.00008659598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004266473,0.0000349869,0.8178754,0.00003313414,0.000007969155,0.0002165365,0.001069365,0.0001709737,0.00008595794,0.00008572006,0.01053369,0.1698436],"study_design_scores_gemma":[0.0007245762,0.00009172501,0.7678055,0.00002033466,0.000008137339,0.00006022739,0.0007145827,0.01595642,0.0002616496,0.01398649,0.1999641,0.0004063224],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8038106,0.000008601137,0.000055721,0.001570907,0.00009504532,0.0002497739,0.00001882187,0.00002891207,0.1941617],"genre_scores_gemma":[0.9903306,0.000129308,0.002096036,0.001391933,0.00006009719,0.00008051653,0.0002057264,0.000020291,0.00568544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1894304,"threshold_uncertainty_score":0.9973757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08633992533550572,"score_gpt":0.2634975760691042,"score_spread":0.1771576507335985,"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."}}