{"id":"W2021735068","doi":"10.1016/j.dam.2008.03.022","title":"Variable space search for graph coloring","year":2008,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":124,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Mathematics; Complete coloring; Fractional coloring; Vertex (graph theory); Graph coloring; Combinatorics; Best-first search; Edge coloring; List coloring; Space (punctuation); Local search (optimization); Variable neighborhood search; Graph; Set (abstract data type); Search algorithm; Greedy coloring; Beam search; Discrete mathematics; Algorithm; Computer science; Metaheuristic; Graph power; Line graph","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":[0.001546674,0.0008330862,0.001763591,0.001620412,0.0008423759,0.00154443,0.001415754,0.00140631,0.006487034],"category_scores_gemma":[0.0073114,0.0006283452,0.001028649,0.002799544,0.001459246,0.002162815,0.00128042,0.002000429,0.0005076134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736787,"about_ca_system_score_gemma":0.002491137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01137438,"about_ca_topic_score_gemma":0.009646769,"domain_scores_codex":[0.9990162,0.0005122176,0.00002720362,0.0001353616,0.0001500136,0.0001589844],"domain_scores_gemma":[0.9957325,0.003601473,0.0001721059,0.0001927638,0.0001696038,0.0001315046],"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":[0.0003466944,0.0001994971,0.0007319249,0.0002631071,0.00008125181,0.0000399445,0.0001518894,0.772967,0.0007042496,0.1078022,0.009151567,0.1075606],"study_design_scores_gemma":[0.00005638928,0.0000385949,0.0001087753,0.00001691964,0.00001219525,0.000009465947,0.0000305062,0.8918463,0.0002078114,0.1066255,0.001041031,0.000006558202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09249604,0.001826825,0.8861065,0.001858202,0.0002113238,0.0001930089,0.0006289262,0.0009726152,0.01570661],"genre_scores_gemma":[0.6065672,0.0009999223,0.376693,0.0004152655,0.0002148729,0.0004107584,0.001031537,0.0003235736,0.01334385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01137438,"threshold_uncertainty_score":0.02261633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.173262291549744,"score_gpt":0.3769068393962284,"score_spread":0.2036445478464845,"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."}}