{"id":"W2055848181","doi":"10.1145/1454503.1454519","title":"Making the best of limited resources","year":2008,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Federation for the Humanities and Social Sciences","keywords":"Cognitive radio; Computer science; Probabilistic logic; Channel (broadcasting); Set (abstract data type); Idle; Range (aeronautics); Differential (mechanical device); Computer network; Telecommunications; Artificial intelligence; Engineering; Wireless","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001849722,0.0008992082,0.001286161,0.0005654632,0.001129795,0.005730239,0.001948415,0.002116427,0.00670622],"category_scores_gemma":[0.009690737,0.0004937078,0.0005262014,0.0008703202,0.001790114,0.004876954,0.003640146,0.001385447,0.001793053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006707198,"about_ca_system_score_gemma":0.001760564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000958495,"about_ca_topic_score_gemma":0.001239261,"domain_scores_codex":[0.9986927,0.0003353255,0.00008231646,0.0003281251,0.0003240078,0.000237525],"domain_scores_gemma":[0.9948438,0.003125464,0.0004906735,0.0008570043,0.0003241039,0.0003589618],"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":[0.000726993,0.0002904988,0.003400782,0.0008662599,0.0005456994,0.001958242,0.0009009851,0.2859374,0.01812157,0.3893157,0.02465742,0.2732785],"study_design_scores_gemma":[0.00007725292,0.0002218226,0.001308301,0.0003180853,0.000174152,0.00100856,0.0009361227,0.4251994,0.005364262,0.5099285,0.05534212,0.0001213721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1424001,0.007071903,0.7213318,0.01572205,0.00146509,0.0001698971,0.0004692007,0.0008673718,0.1105027],"genre_scores_gemma":[0.9130258,0.004068455,0.06348407,0.001037773,0.0005658872,0.0001218442,0.0001816295,0.0001308234,0.01738378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00670622,"threshold_uncertainty_score":0.02243447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04829132688544202,"score_gpt":0.2566805694881168,"score_spread":0.2083892426026748,"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."}}