{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000887039,0.00004664315,0.00006727294,0.00003091282,0.00001278497,0.000005748876,0.0003691368,0.00001805113,0.0002697076],"category_scores_gemma":[0.00003461042,0.00004212393,0.000008161063,0.0001842935,0.00001282631,0.000112856,0.00007096102,0.00003746713,0.00005326276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004020647,"about_ca_system_score_gemma":0.00001056575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001211149,"about_ca_topic_score_gemma":0.0000167781,"domain_scores_codex":[0.999716,0.00000878534,0.0001109406,0.0000565343,0.00003802434,0.0000697128],"domain_scores_gemma":[0.9987417,0.00001557658,0.000009432449,0.001177257,0.00003400108,0.00002197095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002411208,0.0009168291,0.01152769,0.0006926731,0.0004600051,0.00001218092,0.0004095504,0.03895807,0.07681297,0.2823635,0.523794,0.06402837],"study_design_scores_gemma":[0.0003117236,0.000003763017,0.005711949,0.00001869076,0.00001860547,0.000004517442,0.00002647711,0.02955915,0.01082633,0.0003960966,0.952924,0.0001986743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2274911,0.005452745,0.1338551,0.0006241603,0.0007721795,0.0002653326,0.0001727887,0.0007129019,0.6306537],"genre_scores_gemma":[0.9867242,0.0004918033,0.01243645,0.00002316462,0.000007522913,0.000001696441,0.00005341848,0.00001103084,0.0002507185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7592331,"threshold_uncertainty_score":0.2953109,"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."}}