{"id":"W2572771694","doi":"10.1049/iet-com.2016.0558","title":"Cooperative composite sequential detection and its application in spectrum sensing","year":2017,"lang":"en","type":"article","venue":"IET Communications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Composite number; Spectrum (functional analysis); Computer science; Pattern recognition (psychology); Algorithm; Artificial intelligence; Physics","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":[0.0002254732,0.00009780705,0.000124582,0.00008392993,0.001192693,0.0004061417,0.0007337073,0.00004717503,6.760529e-7],"category_scores_gemma":[0.00003669279,0.0001087097,0.00002478352,0.0001458739,0.0001116172,0.0005394364,0.0006065606,0.0002352458,0.00001119034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006663448,"about_ca_system_score_gemma":0.00002524904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000330258,"about_ca_topic_score_gemma":0.00504482,"domain_scores_codex":[0.9991978,0.0001361074,0.0001715184,0.0002425321,0.00008545286,0.0001665859],"domain_scores_gemma":[0.9982221,0.0001078033,0.0001323985,0.001414475,0.00007418238,0.00004908203],"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":[0.00003115303,0.000218264,0.003116992,0.00001787811,0.0000752906,0.00002115681,0.004091875,0.0006451254,0.1706035,0.2008886,0.00002523091,0.620265],"study_design_scores_gemma":[0.0003194359,0.00002309937,0.03185275,0.0000392142,0.00000709191,0.00004703891,0.00002850739,0.9564043,0.006875634,0.003507828,0.0007327266,0.0001623995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.274284,0.0007062479,0.704779,0.01151258,0.0001826104,0.0005334326,0.000004045229,0.0001476365,0.007850458],"genre_scores_gemma":[0.9938276,0.0002933202,0.005706311,0.00008756916,0.00004645853,0.000006486987,0.000004478079,0.000007139221,0.00002068214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9557592,"threshold_uncertainty_score":0.9173349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999264915324598,"score_gpt":0.2907566351126995,"score_spread":0.2607639859594535,"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."}}