{"id":"W2279683949","doi":"10.1007/978-3-642-38457-8_28","title":"Preference Thresholds Optimization by Interactive Variation","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Weighting; Cluster analysis; Similarity (geometry); Preference; Computer science; Variation (astronomy); Cluster (spacecraft); Statistics; Data mining; Artificial intelligence; Mathematics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624904,0.0009052456,0.001664504,0.0008752358,0.0005257309,0.001442638,0.002313873,0.00108389,0.00846514],"category_scores_gemma":[0.005890988,0.0006825114,0.001367344,0.001592758,0.001089415,0.001723377,0.002593042,0.002275429,0.001058263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001184271,"about_ca_system_score_gemma":0.0007441789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00169113,"about_ca_topic_score_gemma":0.001302084,"domain_scores_codex":[0.9987556,0.0005414235,0.00004346916,0.0001953925,0.0003184213,0.0001458309],"domain_scores_gemma":[0.998575,0.0009692716,0.00006554437,0.0001653463,0.00013878,0.00008599916],"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":[0.0002393476,0.0001599089,0.0003588934,0.0002358833,0.000132805,0.00009629536,0.0001919346,0.4156154,0.006253901,0.3122532,0.01210458,0.252358],"study_design_scores_gemma":[0.00002640864,0.00006370278,0.00009333026,0.00001513285,0.00001526464,0.0000399582,0.00001301595,0.8997794,0.0007902426,0.09646734,0.002679458,0.00001677658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006321668,0.0002644914,0.9856845,0.0001301756,0.0000642952,0.00003370455,0.00003294231,0.0002533904,0.007214857],"genre_scores_gemma":[0.471349,0.0004631414,0.5076665,0.0002427813,0.0002086609,0.0003411627,0.000193321,0.0007934373,0.01874183],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00846514,"threshold_uncertainty_score":0.0283187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774783640925353,"score_gpt":0.2282443553129761,"score_spread":0.2104965189037226,"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."}}