{"id":"W2784087652","doi":"10.1007/978-3-319-50137-6_12","title":"The Inductive Constraint Programming Loop","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Constraint programming; Computer science; Concurrent constraint logic programming; Inductive programming; Constraint satisfaction; Exploit; Constraint (computer-aided design); Reactive programming; Scheduling (production processes); Constraint logic programming; Mathematical optimization; Programming language; Theoretical computer science; Programming paradigm; Artificial intelligence; Functional logic programming; Stochastic programming; Mathematics","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":[0.001688902,0.0008040595,0.0005678638,0.001036188,0.00137986,0.002723422,0.002638106,0.0006879396,0.03267469],"category_scores_gemma":[0.006977444,0.0007684749,0.0009238885,0.001457744,0.003203863,0.005030769,0.003149254,0.004054661,0.009449166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001625154,"about_ca_system_score_gemma":0.001936198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576773,"about_ca_topic_score_gemma":0.001757884,"domain_scores_codex":[0.9983591,0.000457377,0.00005939976,0.0003413451,0.0006649804,0.0001178415],"domain_scores_gemma":[0.9972883,0.001725058,0.00007141,0.0005010894,0.0003522524,0.00006184973],"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":[0.00002537397,0.00003066242,0.0000733316,0.0001140477,0.000008582188,0.00003574538,0.0001693118,0.001627279,0.0009119135,0.8752689,0.01459961,0.1071352],"study_design_scores_gemma":[0.00001309594,0.000008492339,0.00005863822,0.00006397031,0.000009864739,0.00005330417,0.00003403132,0.009132019,0.003018157,0.8803264,0.107268,0.00001399181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001937787,0.0005746929,0.8203323,0.001254058,0.0002706829,0.00009674858,0.0002310544,0.001279173,0.1740235],"genre_scores_gemma":[0.125844,0.001921215,0.7677661,0.00190519,0.0007796877,0.0005414224,0.0012152,0.00196577,0.09806143],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03267469,"threshold_uncertainty_score":0.1093078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546279132236073,"score_gpt":0.2415703809151606,"score_spread":0.2261075895927998,"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."}}