{"id":"W4252443822","doi":"10.1198/004017004000000374","title":"Scale Counting","year":2004,"lang":"en","type":"article","venue":"Technometrics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Statistics; Mathematics; Computer science","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":[0.01235959,0.001710473,0.00190483,0.00588417,0.002679362,0.005042418,0.00363479,0.001796867,0.01833195],"category_scores_gemma":[0.102659,0.0007155641,0.001758502,0.008890977,0.002692701,0.006007712,0.00451221,0.00215266,0.00689711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421683,"about_ca_system_score_gemma":0.001407969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002364446,"about_ca_topic_score_gemma":0.002851197,"domain_scores_codex":[0.9802369,0.005146178,0.001433093,0.002824974,0.009812422,0.0005464079],"domain_scores_gemma":[0.9570918,0.0151608,0.003170226,0.0140852,0.009921749,0.0005702231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002429157,0.0001031592,0.009041605,0.0009999839,0.0001761143,0.0002712335,0.001907143,0.009754773,0.00731862,0.304229,0.03583828,0.6301172],"study_design_scores_gemma":[0.000125447,0.0003220886,0.01558859,0.0007927358,0.000283279,0.001399607,0.002020121,0.1320979,0.02575258,0.5265987,0.2946153,0.000403713],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005922226,0.0006425781,0.9706344,0.0003720887,0.0005183095,0.0004245295,0.000639813,0.001419692,0.0194263],"genre_scores_gemma":[0.1340795,0.001075218,0.8469308,0.000456878,0.0004260853,0.001274219,0.00132542,0.0009983166,0.01343359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01833195,"threshold_uncertainty_score":0.06536454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1687207083646495,"score_gpt":0.4074914508798365,"score_spread":0.238770742515187,"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."}}