{"id":"W2954987224","doi":"10.1007/978-3-030-22999-3_72","title":"Pareto Optimality for Conditional Preference Networks with Comfort","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Preference; Pareto optimal; Computer science; Pareto principle; Mathematical optimization; Outcome (game theory); Multi-objective optimization; Mathematical economics; Mathematics; Machine learning; Statistics","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":[0.003849621,0.001448577,0.001601229,0.001539469,0.001406151,0.003441043,0.002024494,0.001550372,0.01699017],"category_scores_gemma":[0.01170285,0.0009507907,0.001885222,0.002643268,0.002685081,0.005480727,0.002966712,0.004377966,0.00119379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003891618,"about_ca_system_score_gemma":0.0018017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003313096,"about_ca_topic_score_gemma":0.003649531,"domain_scores_codex":[0.9975961,0.001156986,0.0001030842,0.0002948383,0.0005306143,0.000318349],"domain_scores_gemma":[0.9944364,0.004178185,0.0002598389,0.000395709,0.000427359,0.0003024726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005029524,0.00003017759,0.0001146451,0.00006686793,0.00002388822,0.00002075916,0.00008204504,0.0583006,0.0002604608,0.9252098,0.002049251,0.01379122],"study_design_scores_gemma":[0.00001208147,0.00001855551,0.0001038122,0.00002790618,0.00001140898,0.00002150936,0.00003618059,0.1198326,0.0001436718,0.8779691,0.001812301,0.00001085048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02630617,0.0005016085,0.9025429,0.0009379925,0.00009743451,0.0001020891,0.0003726817,0.0001098117,0.06902931],"genre_scores_gemma":[0.6804238,0.001957627,0.2502604,0.0006056464,0.0003882893,0.0008666693,0.0009099246,0.0003256498,0.06426205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01699017,"threshold_uncertainty_score":0.05683786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364771067942558,"score_gpt":0.2384754124040585,"score_spread":0.2148277017246329,"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."}}