{"id":"W4405718667","doi":"10.23919/emc.2003.10806290","title":"Update of VHF Business Noise Data","year":2003,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Noise (video); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002108389,0.0004931782,0.0004945644,0.007419339,0.0007952254,0.001976905,0.001079057,0.0006857132,0.006676296],"category_scores_gemma":[0.007473447,0.0002578097,0.0002994313,0.004039495,0.0002998384,0.001060476,0.0008086459,0.001068036,0.007454753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002306039,"about_ca_system_score_gemma":0.002364552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1220975,"about_ca_topic_score_gemma":0.1549487,"domain_scores_codex":[0.9982009,0.0001014538,0.0001498918,0.0002026861,0.001153765,0.0001912819],"domain_scores_gemma":[0.9832429,0.0009749015,0.0006470474,0.002402219,0.01202189,0.0007109486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004361335,0.0002955979,0.09766587,0.0005516437,0.0001312929,0.0006137139,0.0006656765,0.004453707,0.01467909,0.002784253,0.3552248,0.5224983],"study_design_scores_gemma":[0.00001795622,0.00007831228,0.1041647,0.000135937,0.00004335242,0.0002593459,0.0001746193,0.0013112,0.003946404,0.0002644253,0.8895612,0.00004247962],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2171799,0.009009767,0.04058167,0.006612704,0.009714532,0.0007062153,0.5856789,0.009205939,0.1213104],"genre_scores_gemma":[0.2168297,0.004479853,0.03369001,0.001658735,0.002510764,0.0002682312,0.6555216,0.001309711,0.0837314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1220975,"threshold_uncertainty_score":0.2427737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03238683220536405,"score_gpt":0.2475443137940297,"score_spread":0.2151574815886656,"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."}}