{"id":"W2153762155","doi":"10.1093/icesjms/fst237","title":"Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods","year":2014,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Stock (firearms); Robustness (evolution); Stock assessment; Computer science; Econometrics; Economics; Engineering; Chemistry; Biology; Ecology; Mechanical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.02014683,0.0001723853,0.0003390225,0.0003079044,0.0006443309,0.0005397568,0.00248565,0.00004447204,0.00003707329],"category_scores_gemma":[0.003907365,0.00008556858,0.0001057238,0.002006473,0.0003465139,0.001231356,0.0005456411,0.0003778025,0.000002181521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001115434,"about_ca_system_score_gemma":0.0004614667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282006,"about_ca_topic_score_gemma":0.00002506922,"domain_scores_codex":[0.9944611,0.0008339985,0.001398281,0.0004425792,0.002620515,0.0002435624],"domain_scores_gemma":[0.9707135,0.02344167,0.002425712,0.0008938352,0.002373197,0.0001520625],"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":[0.00003277002,0.00005984943,0.0008140036,9.23981e-7,0.000007306634,2.19902e-7,0.0003537751,0.9193139,0.0008043118,0.006064873,0.00003885794,0.07250918],"study_design_scores_gemma":[0.0002561809,0.000337041,0.0555473,0.00004547404,0.00001824234,0.000001426156,0.0009998817,0.8244529,0.0003502623,0.1177078,0.0001901676,0.00009333797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4372837,0.000007913304,0.5531441,0.002543851,0.0002166932,0.0003913361,0.0000198512,0.00001612886,0.006376413],"genre_scores_gemma":[0.812753,0.000002085735,0.1867386,0.0002928545,0.0001690475,0.00001026796,0.000001074126,0.000006181727,0.00002692448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3754693,"threshold_uncertainty_score":0.698253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4764724235639992,"score_gpt":0.5361010977007016,"score_spread":0.05962867413670242,"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."}}