{"id":"W2021662179","doi":"10.1287/opre.1040.0158","title":"Selecting Attributes to Measure the Achievement of Objectives","year":2005,"lang":"en","type":"article","venue":"Operations Research","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":369,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"U.S. Department of Energy","keywords":"Measure (data warehouse); Computer science; Statement (logic); Proxy (statistics); Foundation (evidence); Meaning (existential); Management science; Problem statement; Operations research; Risk analysis (engineering); Data mining; Machine learning; Mathematics; Business","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":[0.01243945,0.00005818098,0.0001416795,0.000393939,0.001142065,0.0003104981,0.0007817164,0.00003091803,0.0004680541],"category_scores_gemma":[0.008331117,0.00003182697,0.00007959948,0.003411319,0.0001080718,0.0002240712,0.000193887,0.0002408439,0.0005790627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000064179,"about_ca_system_score_gemma":0.0001916221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003487209,"about_ca_topic_score_gemma":0.005737465,"domain_scores_codex":[0.9958543,0.0009451442,0.0003983238,0.0002766265,0.002267152,0.0002584927],"domain_scores_gemma":[0.9957412,0.001515667,0.00002045158,0.0005594956,0.002082727,0.00008045061],"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":[0.00007527022,0.0003528695,0.01616314,0.000002300752,0.000130795,0.00000109839,0.01529922,0.4017927,0.03832845,0.01557969,0.02827576,0.4839987],"study_design_scores_gemma":[0.001062594,0.0009335992,0.175414,0.00007088664,0.00005601742,0.000009340367,0.06371969,0.2314105,0.1730366,0.009589164,0.344065,0.0006326654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7945226,0.0004986821,0.0281776,0.1615462,0.00005413452,0.000725851,0.00002683036,0.00001832164,0.01442977],"genre_scores_gemma":[0.9891732,0.00004139696,0.002929229,0.00008680305,0.0001088687,0.00004198234,0.000001656409,0.000004052614,0.007612798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.483366,"threshold_uncertainty_score":0.9973727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3172619526135115,"score_gpt":0.5063036430112521,"score_spread":0.1890416903977406,"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."}}