{"id":"W4393380048","doi":"10.1109/ecrime61234.2023.10485551","title":"A Kubernetes Underlay for OpenTDIP Forensic Computing Backend","year":2023,"lang":"en","type":"article","venue":"","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Underlay; Computer science; Forensic science; Telecommunications; Signal-to-noise ratio (imaging)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001559629,0.001071049,0.0009645995,0.001352316,0.001211656,0.004674083,0.004719155,0.001355807,0.01694911],"category_scores_gemma":[0.004815254,0.0009661617,0.0007013361,0.0008407283,0.0013041,0.005435222,0.008800326,0.002453622,0.005591576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149332,"about_ca_system_score_gemma":0.00126801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001489636,"about_ca_topic_score_gemma":0.00161348,"domain_scores_codex":[0.997972,0.0002545587,0.000179583,0.0005594192,0.0006466046,0.0003877983],"domain_scores_gemma":[0.9960373,0.0005029035,0.0002297268,0.002129554,0.0006373826,0.0004631389],"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":[0.01296723,0.002036387,0.02932249,0.001805081,0.0008838169,0.003965788,0.002273505,0.05086168,0.1889643,0.09877151,0.1582366,0.4499116],"study_design_scores_gemma":[0.0005853858,0.001153462,0.007267933,0.0003380652,0.000353903,0.001819322,0.0005685604,0.5343698,0.2226225,0.03823102,0.1922326,0.0004574506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1183595,0.0009121737,0.7098362,0.001075265,0.0009934003,0.0007678178,0.001530884,0.137255,0.02926967],"genre_scores_gemma":[0.7691931,0.0003717136,0.1935555,0.001361108,0.0002243239,0.000594906,0.003958318,0.006407112,0.02433383],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01694911,"threshold_uncertainty_score":0.05670047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03335984558005917,"score_gpt":0.2666832984502488,"score_spread":0.2333234528701896,"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."}}