{"id":"W4412792239","doi":"10.1145/3754456","title":"Every Breath You Don’t Take: Deepfake Speech Detection Using Breath","year":2025,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001425952,0.0001988009,0.000199398,0.000397538,0.0006866116,0.003104708,0.0005402542,0.000121267,0.00000805216],"category_scores_gemma":[0.001667561,0.0001800261,0.00005105013,0.0012712,0.0001763707,0.006374686,0.0005488224,0.0005303402,0.00004191492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530427,"about_ca_system_score_gemma":0.0003660994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001725531,"about_ca_topic_score_gemma":0.00002548694,"domain_scores_codex":[0.9974762,0.0001820086,0.0002416733,0.000683198,0.0007247109,0.0006921846],"domain_scores_gemma":[0.9972823,0.00131598,0.00008901223,0.0005305794,0.0005606625,0.0002214323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001969889,0.0001641547,0.0008999796,0.00005565586,0.00005262709,0.0001937013,0.0001779078,0.000005427471,0.003729578,0.0007101399,0.0001597763,0.9936541],"study_design_scores_gemma":[0.005321868,0.00273727,0.009222311,0.001499633,0.0001546784,0.0105834,0.004444267,0.02021139,0.4815106,0.2348703,0.2268555,0.002588783],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2888514,0.01016891,0.3767431,0.005829572,0.0006094438,0.0008606923,0.00002366789,0.0005011795,0.3164121],"genre_scores_gemma":[0.9759079,0.0006050703,0.01777132,0.0003170796,0.0001544696,0.00001330655,0.000006199216,0.00001868018,0.00520603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9910653,"threshold_uncertainty_score":0.9979302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0860815806185097,"score_gpt":0.3941075366942001,"score_spread":0.3080259560756904,"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."}}