{"id":"W4318603555","doi":"10.1109/ssci51031.2022.10022070","title":"Mother Tree Optimization for Conditional Constraints and Qualitative Preferences","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"A priori and a posteriori; Set (abstract data type); Mathematical optimization; Constraint satisfaction problem; Constraint (computer-aided design); Tree (set theory); Computer science; Mathematics; Artificial intelligence; Combinatorics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000515323,0.0002351067,0.0002160194,0.0002778568,0.0008331166,0.0002215925,0.0004113706,0.00005310056,0.00092364],"category_scores_gemma":[0.0000577661,0.0002595054,0.00008460478,0.0004575966,0.0003552732,0.0006981628,0.0001207042,0.0001979615,0.0000126895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372751,"about_ca_system_score_gemma":0.0002194176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008734711,"about_ca_topic_score_gemma":0.00001330448,"domain_scores_codex":[0.997752,0.0002920342,0.0004534883,0.0006291425,0.0006177477,0.0002555704],"domain_scores_gemma":[0.9984131,0.0007520457,0.0002524666,0.0001893576,0.0002829014,0.0001101156],"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":[0.0000593831,0.00007001286,0.00003149449,0.00001199867,0.00003511968,0.000001660221,0.003060777,0.7595401,0.00005379041,0.2283589,0.000618437,0.008158372],"study_design_scores_gemma":[0.0003049773,0.0006811879,0.0001653256,0.0000107588,0.0000112701,0.00006472375,0.003523668,0.9191244,0.0003976836,0.07456675,0.0007937223,0.000355588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00173257,0.00002633012,0.9912733,0.003719106,0.000682428,0.0005927206,0.0003376927,0.0001484764,0.001487399],"genre_scores_gemma":[0.8355814,0.00004414938,0.1613937,0.001270777,0.00008083406,0.0004576904,0.0005210279,0.00002490164,0.0006255407],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8338488,"threshold_uncertainty_score":0.9999896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213036417730307,"score_gpt":0.3027704748620733,"score_spread":0.2706401106847702,"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."}}