{"id":"W7117896362","doi":"10.1613/jair.1.19533","title":"Combining Constraint Programming and Machine Learning: From Current Progress to Future Opportunities","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Constraint programming; Key (lock); Resource (disambiguation); Constraint (computer-aided design); Resource constraints; Open research","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":[0.01546628,0.002019541,0.002776307,0.004567009,0.001144871,0.01025194,0.004453679,0.003939042,0.007549338],"category_scores_gemma":[0.0249314,0.001659777,0.001806423,0.01551492,0.004889225,0.02577124,0.006380538,0.009932193,0.002401415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003235531,"about_ca_system_score_gemma":0.005942097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004158449,"about_ca_topic_score_gemma":0.004251909,"domain_scores_codex":[0.9910911,0.004101295,0.0005382877,0.0009606853,0.002914138,0.0003944792],"domain_scores_gemma":[0.9612495,0.03216452,0.0007778745,0.001744928,0.003279961,0.000783294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009371449,0.0001291118,0.001011192,0.006944271,0.0002360667,0.000126276,0.0005483917,0.007995124,0.0003960539,0.2843301,0.02509914,0.6730905],"study_design_scores_gemma":[0.000031584,0.0001196994,0.0007531536,0.0060133,0.0001146099,0.0004271472,0.001113778,0.03756401,0.0008737367,0.5298343,0.4230014,0.0001532961],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002007256,0.8128383,0.1421503,0.02471133,0.001090901,0.00005156426,0.0001425504,0.0003077156,0.0167002],"genre_scores_gemma":[0.02687262,0.826269,0.1330435,0.004610146,0.005829539,0.0001305698,0.0003591939,0.000294096,0.002591336],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01546628,"threshold_uncertainty_score":0.0817945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2147351455846759,"score_gpt":0.4125603237854467,"score_spread":0.1978251782007708,"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."}}