{"id":"W1529504427","doi":"10.1023/a:1011858020793","title":"Design of an Efficient Channel Block Retuning","year":2001,"lang":"en","type":"article","venue":"Mobile Networks and Applications","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Channel (broadcasting); Discretization; Tabu search; Interference (communication); Mathematical optimization; Heuristic; Cellular network; Co-channel interference; Algorithm; Telecommunications; Mathematics; 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":[0.0004598574,0.00009889658,0.0001358306,0.00007692331,0.0002745813,0.00008470428,0.0009314489,0.00006735366,0.000005086061],"category_scores_gemma":[0.000003833848,0.00009610779,0.00002356825,0.000759501,0.00009995444,0.000114215,0.0003364209,0.000170932,0.00000414095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001665935,"about_ca_system_score_gemma":0.00002824436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001177615,"about_ca_topic_score_gemma":0.000002395129,"domain_scores_codex":[0.9989093,0.0001250883,0.0002320128,0.0003043845,0.0001744738,0.0002547566],"domain_scores_gemma":[0.9983529,0.0002168765,0.00009743225,0.001078321,0.0001216568,0.0001327581],"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":[0.000003065076,0.0001102082,0.00002352604,0.000002608006,0.000004866478,5.270701e-7,0.00006188891,0.9482172,0.00008557827,0.006617458,0.00007925019,0.04479384],"study_design_scores_gemma":[0.0001311944,0.00006215429,0.0002039819,0.00001260879,0.00000280712,0.00001325237,0.00003280567,0.9948083,0.00005699887,0.0003423942,0.004235754,0.0000977026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006213007,0.001035808,0.9913591,0.0001326621,0.00001917131,0.0007033875,6.340764e-7,0.0001004641,0.0004358144],"genre_scores_gemma":[0.9885513,0.0008670997,0.009375374,0.00004904885,0.00009386528,0.0009727581,0.000006866976,0.0000111847,0.00007251291],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9823383,"threshold_uncertainty_score":0.3919162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02621361423917903,"score_gpt":0.2802323923317478,"score_spread":0.2540187780925688,"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."}}