{"id":"W2547383779","doi":"10.1145/2989250.2989265","title":"Stay or Switch?","year":2016,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Agence Nationale pour le Développement de la Recherche en Santé","keywords":"Computer science; Metric (unit); Key (lock); Underlay; Queueing theory; Cognitive radio; Focus (optics); Exploit; Performance metric; Function (biology); Range (aeronautics); Point (geometry); Computer network; Distributed computing; Topology (electrical circuits); Telecommunications; Signal-to-noise ratio (imaging); Computer security; Mathematics","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":[0.001151063,0.0003079324,0.0004017104,0.0004506624,0.001962025,0.003136708,0.001017261,0.001618473,0.0334577],"category_scores_gemma":[0.005678703,0.0001812739,0.0003984198,0.0004555447,0.001655675,0.004221754,0.001680234,0.001850336,0.006183193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009667879,"about_ca_system_score_gemma":0.0008316308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002192887,"about_ca_topic_score_gemma":0.003247465,"domain_scores_codex":[0.9990007,0.0002503942,0.00002668404,0.0002812917,0.0001432341,0.0002976313],"domain_scores_gemma":[0.9976645,0.0007924945,0.000225,0.0003014059,0.0002288743,0.0007878469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001153993,0.0004925222,0.0229516,0.0003226834,0.0001378296,0.002120874,0.005336752,0.00285419,0.005678189,0.3524552,0.1605608,0.4459353],"study_design_scores_gemma":[0.0001045651,0.0003587293,0.01377406,0.0002375243,0.0001007334,0.002859677,0.0127011,0.01126586,0.003148641,0.3866676,0.568651,0.0001304526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2942156,0.004478187,0.07395606,0.0810094,0.005244952,0.0002359979,0.001463377,0.001795154,0.5376012],"genre_scores_gemma":[0.9158538,0.001268356,0.007303319,0.0113158,0.0005985594,0.0001056895,0.0004513109,0.0002509251,0.06285232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0334577,"threshold_uncertainty_score":0.1119272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0181701859583318,"score_gpt":0.2382799073703431,"score_spread":0.2201097214120113,"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."}}