{"id":"W7005999274","doi":"","title":"Speech security thresholds for closed offices","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Marine Biology and Environmental Chemistry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Public Works and Government Services Canada","keywords":"Range (aeronautics); Voice activity detection; Rhythm; Speech processing; Active listening","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001103978,0.0005943241,0.00048515,0.001316503,0.0004090473,0.0008160718,0.0004040337,0.0007244028,0.006826373],"category_scores_gemma":[0.01310089,0.0002129231,0.0004872162,0.0002717297,0.0008493731,0.00100855,0.001158507,0.0009415833,0.001084631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002614145,"about_ca_system_score_gemma":0.0002442049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007547573,"about_ca_topic_score_gemma":0.0005653902,"domain_scores_codex":[0.9983751,0.0002415582,0.0002595506,0.0002317973,0.0006591436,0.0002329332],"domain_scores_gemma":[0.9874198,0.006689724,0.001928011,0.0007607703,0.00180249,0.001399279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02746128,0.001747434,0.2137803,0.001170547,0.0002538509,0.003140471,0.009462573,0.002371367,0.606112,0.002410919,0.001939074,0.1301503],"study_design_scores_gemma":[0.0002207641,0.01535363,0.6997276,0.0002286764,0.0003492267,0.004907337,0.00314264,0.003011374,0.2649553,0.003614526,0.004267767,0.0002212788],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898997,0.0006367228,0.003398039,0.00006835216,0.00006812927,0.00006790146,0.000350521,0.00009886098,0.005411837],"genre_scores_gemma":[0.9981578,0.0001021327,0.0006852302,0.00004790583,0.00001412112,0.00002952845,0.0002480459,0.00002210318,0.0006930801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006826373,"threshold_uncertainty_score":0.02283651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006183005025912896,"score_gpt":0.1958986130649262,"score_spread":0.1897156080390133,"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."}}