{"id":"W2081336934","doi":"10.1016/j.dam.2013.11.018","title":"Directed weighted improper coloring for cellular channel allocation","year":2013,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis","funders":"","keywords":"Mathematics; Combinatorics; Vertex (graph theory); Fractional coloring; Integer (computer science); Complete coloring; Edge coloring; Discrete mathematics; Graph coloring; Channel (broadcasting); Graph; Integer programming; Set (abstract data type); Algorithm; Computer science; Line graph; Graph power","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":[0.00006509506,0.0002133614,0.000230731,0.00005956927,0.00008099835,0.00004965492,0.0001351563,0.000103105,0.00004174291],"category_scores_gemma":[0.00001142896,0.0001995392,0.00004382388,0.0001945586,0.00002286448,0.0001538767,0.00002646678,0.00008962646,0.00008244815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006275829,"about_ca_system_score_gemma":0.000005915017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001319667,"about_ca_topic_score_gemma":9.353931e-7,"domain_scores_codex":[0.9990677,0.000003248884,0.000312452,0.0001784545,0.00012638,0.0003117891],"domain_scores_gemma":[0.9994425,0.00007026412,0.00007112417,0.0002637987,0.00008248717,0.00006985236],"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":[0.00001407918,0.00008304878,0.000002167694,0.001152441,0.0001589119,5.655337e-7,0.001913666,0.5746847,0.3858657,0.02750617,0.002555133,0.006063406],"study_design_scores_gemma":[0.0003091766,0.00001063335,0.000005028831,0.00003204707,0.00002682529,4.269835e-7,0.0001606784,0.9279621,0.06260819,0.008347427,0.0002801192,0.0002573106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0323538,0.00006825216,0.9602745,0.00002385846,0.0001393845,0.001496842,0.000006811074,0.0009578497,0.004678712],"genre_scores_gemma":[0.7229828,0.00004577827,0.2748305,0.00001823128,0.0001375207,0.00151945,0.0001687966,0.0001335298,0.000163387],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.690629,"threshold_uncertainty_score":0.8136973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008197128570197996,"score_gpt":0.1945219894499761,"score_spread":0.1863248608797781,"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."}}