{"id":"W2117931398","doi":"10.1016/j.icesjms.2004.11.015","title":"Local influence diagnostics for the retrospective problem in sequential population analysis","year":2005,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Fisheries and Oceans Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Retrospective cohort study; Stock (firearms); Stock assessment; Population; Statistics; Geography; Demography; Fishery; Medicine; Biology; Mathematics; Fishing; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02553838,0.0008256256,0.00121232,0.002406758,0.0007940317,0.001337106,0.001717093,0.001510602,0.001395439],"category_scores_gemma":[0.2281448,0.0006237086,0.001324769,0.001317692,0.002884899,0.001932581,0.002117009,0.001934405,0.0001529501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371744,"about_ca_system_score_gemma":0.000959558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004846771,"about_ca_topic_score_gemma":0.003010647,"domain_scores_codex":[0.9848007,0.01050347,0.0007961289,0.001556054,0.001980054,0.0003636628],"domain_scores_gemma":[0.6609699,0.3063937,0.01586084,0.007772605,0.007576416,0.00142649],"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":[0.0007326232,0.0001550426,0.1434905,0.000349543,0.000679073,0.001446024,0.00104829,0.5896484,0.002549845,0.1475598,0.002231978,0.110109],"study_design_scores_gemma":[0.00003675037,0.000133657,0.005450105,0.00002158268,0.00004130131,0.000142086,0.00004434802,0.9625152,0.001050907,0.03011412,0.0004219557,0.00002797856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08302271,0.0003260948,0.9146233,0.0003587204,0.00004203261,0.000122577,0.00009631331,0.0003329942,0.001075385],"genre_scores_gemma":[0.8824815,0.0001502232,0.1159739,0.0001729155,0.0001120551,0.0001982575,0.000214834,0.00006978711,0.0006265702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02553838,"threshold_uncertainty_score":0.1350615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01093126310661637,"score_gpt":0.2831569501675755,"score_spread":0.2722256870609591,"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."}}