{"id":"W3182975606","doi":"10.5539/cis.v14n3p25","title":"Verifying the Audio Evidence to Assist Forensic Investigation","year":2021,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Authentication (law); Presentation (obstetrics); Computer forensics; Computer security; Field (mathematics); Multimedia; Digital forensics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0007244711,0.00008790753,0.00007922855,0.0001709094,0.0004091418,0.001819883,0.0006279442,0.00002081981,0.000001503557],"category_scores_gemma":[0.0004552472,0.00006564768,0.00002216931,0.001889587,0.0002668115,0.01684741,0.0006653561,0.00008414426,0.00009467269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000614652,"about_ca_system_score_gemma":0.0002565786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000489553,"about_ca_topic_score_gemma":0.000002851132,"domain_scores_codex":[0.9987193,0.00002538434,0.0002411398,0.0002297646,0.0005701393,0.0002142381],"domain_scores_gemma":[0.9987311,0.0001279707,0.00008453199,0.0004404255,0.0004529285,0.0001630415],"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":[0.000001703848,0.000003250666,0.0004637613,0.00001609968,0.000002259176,0.000001501298,0.003904158,0.0004794199,0.0005753455,0.06136999,0.001413357,0.9317691],"study_design_scores_gemma":[0.0003351777,0.0002672466,0.2281865,0.0003564665,0.000007509099,0.0003667844,0.0002775232,0.6719397,0.05228153,0.007809685,0.03763529,0.0005365314],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08904012,0.00003420483,0.903206,0.004080213,0.00158234,0.000141545,5.528418e-7,0.0001039042,0.001811114],"genre_scores_gemma":[0.9335591,0.00001147615,0.05991483,0.006401555,0.00007667737,0.00001246031,0.000001258528,0.000001819659,0.00002087762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9312326,"threshold_uncertainty_score":0.9992163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02665471100536909,"score_gpt":0.2455579590614438,"score_spread":0.2189032480560748,"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."}}