{"id":"W2116751471","doi":"10.1109/ccece.1999.807165","title":"Overlaid cellular system design with cell selection criteria for mobile wireless users","year":2003,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Wireless; Cellular radio; Mobile telephony; Computer network; Mobile radio; Telecommunications; Base station; 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":[0.0008560892,0.0001687919,0.0001898388,0.0001190118,0.0003037296,0.0003270468,0.001025477,0.00008545619,0.00002775683],"category_scores_gemma":[0.000009665838,0.0001417044,0.00005027945,0.0006775915,0.00004203078,0.0004805709,0.00009714803,0.0001508959,0.00003336825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001748473,"about_ca_system_score_gemma":0.0001899684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002445198,"about_ca_topic_score_gemma":0.000008983628,"domain_scores_codex":[0.9980739,0.0004774306,0.0002333257,0.0004268239,0.0003438645,0.0004446912],"domain_scores_gemma":[0.9982119,0.0003411005,0.00009030693,0.0009211345,0.0003017395,0.0001337906],"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":[0.0003516242,0.001105041,0.001300351,0.001060855,0.0001930946,0.00002867345,0.002116841,0.1379249,0.2283942,0.5795687,0.03287625,0.01507943],"study_design_scores_gemma":[0.0006634002,0.0003758712,0.00001351886,0.00003380794,0.000005652911,0.00001540512,0.0001818254,0.8064811,0.1868317,0.00007757995,0.005077888,0.0002423394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01314026,0.0001038561,0.9815371,0.00004425841,0.0001126823,0.00108702,8.027707e-7,0.0003305327,0.00364348],"genre_scores_gemma":[0.7708234,0.0000112813,0.2275527,0.00003102257,0.00002266623,0.00046613,0.00000230379,0.00002214639,0.001068382],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7576832,"threshold_uncertainty_score":0.5778536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03169098539941923,"score_gpt":0.2733270892826097,"score_spread":0.2416361038831905,"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."}}