{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003657522,0.0003677526,0.0004479704,0.0005717229,0.0001767964,0.000404808,0.0006094229,0.0005711784,0.0005236596],"category_scores_gemma":[0.00787132,0.0002666759,0.0005594397,0.0003495862,0.0002755948,0.0006742465,0.0003694322,0.0004931042,0.0001110233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005719755,"about_ca_system_score_gemma":0.0007077152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01971288,"about_ca_topic_score_gemma":0.0179023,"domain_scores_codex":[0.9995772,0.000238492,0.00002225006,0.00007448222,0.00005211756,0.0000354873],"domain_scores_gemma":[0.9947353,0.004050658,0.0005458587,0.0001690921,0.0003762576,0.0001228613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003036919,0.00001516058,0.009547742,0.000006444759,0.00002661192,0.0000102825,0.00001897723,0.9864171,0.0002280491,0.0004208266,0.00005680582,0.003221639],"study_design_scores_gemma":[0.000003257132,0.00001574229,0.001560068,0.000002479811,0.000002719311,0.000002352028,0.000004867106,0.9976865,0.00007511185,0.0006203463,0.00002383816,0.000002730879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8556724,0.00009059152,0.1428502,0.0001406466,0.00001883894,0.00003789331,0.0002528825,0.0001277406,0.0008088229],"genre_scores_gemma":[0.9868481,0.00003792285,0.01261829,0.00001941607,0.000006556928,0.00002584242,0.0001379001,0.000004712421,0.0003012641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01971288,"threshold_uncertainty_score":0.03919631,"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."}}