{"id":"W2952340783","doi":"10.48550/arxiv.1103.3745","title":"The AllDifferent Constraint with Precedences","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Local consistency; Constraint (computer-aided design); Constraint programming; Constraint logic programming; Consistency (knowledge bases); Decomposition method (queueing theory); Constraint satisfaction problem; Constraint satisfaction; Constraint graph; Binary constraint; Computer science; Mathematical optimization; Mathematics; Theoretical computer science; Discrete mathematics; Probabilistic logic; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001591513,0.000224124,0.0001661833,0.00008771297,0.0003071017,0.0002201283,0.001207607,0.0001360356,0.00008413082],"category_scores_gemma":[0.00001566995,0.0001643374,0.00009293492,0.0002277153,0.0003245936,0.0002121743,0.0006779428,0.0003889723,0.00003609414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009035158,"about_ca_system_score_gemma":0.0002651877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006620488,"about_ca_topic_score_gemma":0.0003643063,"domain_scores_codex":[0.9987585,0.0001178792,0.0001427174,0.000645952,0.00008971152,0.000245221],"domain_scores_gemma":[0.9985747,0.0001190971,0.0002224819,0.0008162382,0.00014754,0.0001199646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000418129,0.0000531442,0.006374373,0.00002288194,0.0001584676,0.0000772225,0.0005129359,0.06044405,0.000004759968,0.9193884,0.0002548391,0.01266709],"study_design_scores_gemma":[0.001210534,0.0002854998,0.03074082,0.000287006,0.0001745906,0.0000665153,0.000896461,0.8190234,0.0003166104,0.1441793,0.001362598,0.001456706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03463709,0.00003374293,0.9485502,0.0002550485,0.0005524446,0.0003268172,0.000004295346,0.0002377365,0.01540258],"genre_scores_gemma":[0.9961882,0.0003146957,0.002469406,0.00004636567,0.00002334912,0.000001666542,0.000003935628,0.000007459534,0.0009449062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9615511,"threshold_uncertainty_score":0.6701486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06913467774796436,"score_gpt":0.1707907165209899,"score_spread":0.1016560387730255,"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."}}