{"id":"W2113374668","doi":"10.1145/332040.332414","title":"Using naming time to evaluate quality predictors for model simplification","year":2000,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Computer science; Quality (philosophy); Measure (data warehouse); Artificial intelligence; Machine learning; Cognition; Data mining; Natural language processing; Psychology","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":[0.01815434,0.00105038,0.001016136,0.002277411,0.0006731754,0.002259494,0.0006829654,0.001069787,0.001972208],"category_scores_gemma":[0.2434913,0.0003887568,0.001277179,0.001912651,0.0009630091,0.004249544,0.001248448,0.00126504,0.000536051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000877167,"about_ca_system_score_gemma":0.0009353418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00257459,"about_ca_topic_score_gemma":0.002682397,"domain_scores_codex":[0.9904612,0.004388617,0.001444575,0.00107236,0.002361209,0.0002719992],"domain_scores_gemma":[0.6269583,0.3085615,0.03033947,0.01600703,0.01550272,0.002631044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005418327,0.001379598,0.6825647,0.0006973429,0.001016777,0.0002484716,0.004336622,0.02823752,0.01840266,0.002497066,0.003404421,0.2517965],"study_design_scores_gemma":[0.000463915,0.005400686,0.7768334,0.00012836,0.000621268,0.0005496013,0.001719181,0.1755863,0.02627842,0.009005195,0.002936979,0.0004766593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281907,0.0006601794,0.06533064,0.0001877512,0.00009022262,0.0004735934,0.000702896,0.0007932552,0.003570735],"genre_scores_gemma":[0.9657208,0.0001879038,0.03145806,0.00005411305,0.00003180681,0.0004673689,0.001378498,0.0001951658,0.0005064099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01815434,"threshold_uncertainty_score":0.09601045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383067402434222,"score_gpt":0.4184403955553931,"score_spread":0.2801336553119709,"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."}}