{"id":"W1997146567","doi":"10.1007/s11269-014-0799-4","title":"Evaluation of the Performance of Eight Record-Extension Techniques Under Different Levels of Association, Presence of Outliers and Different Sizes of Concurrent Records: A Monte Carlo Study","year":2014,"lang":"en","type":"article","venue":"Water Resources Management","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Ste. Anne's Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Outlier; Statistics; Percentile; Variance (accounting); Extension (predicate logic); Computer science; Ordinary least squares; Monte Carlo method; Contrast (vision); Data mining; Mathematics; Artificial intelligence; Accounting","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":[0.007682502,0.0008076344,0.001223351,0.001121197,0.0007998007,0.000977952,0.001670131,0.001439758,0.0009211685],"category_scores_gemma":[0.02424018,0.0003826085,0.001067137,0.001661225,0.000530507,0.001914451,0.001116494,0.0009655258,0.0002211651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180997,"about_ca_system_score_gemma":0.001533474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004879877,"about_ca_topic_score_gemma":0.003972678,"domain_scores_codex":[0.997667,0.0008766545,0.0003003229,0.0005014161,0.0004633513,0.000191339],"domain_scores_gemma":[0.9525964,0.03738846,0.002398499,0.003674749,0.003231167,0.0007107184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007682607,0.00203483,0.05895911,0.0006512901,0.0009540419,0.0002329213,0.000669363,0.4198853,0.02237008,0.002157078,0.0006739802,0.4837295],"study_design_scores_gemma":[0.0001964786,0.001672178,0.01343357,0.00003242331,0.0003780962,0.0002637466,0.0002269422,0.9668687,0.015194,0.0009933428,0.0006660452,0.0000745767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.852865,0.001208811,0.144071,0.0001261196,0.00004141484,0.0001575195,0.0002080901,0.0007026158,0.0006195417],"genre_scores_gemma":[0.7885934,0.0004549593,0.209486,0.00003292416,0.00002831762,0.00008658426,0.0004750078,0.00006726098,0.0007755732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007682502,"threshold_uncertainty_score":0.04062945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0835099427494799,"score_gpt":0.3554605009844133,"score_spread":0.2719505582349334,"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."}}