{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001729982,0.0001161583,0.0001322103,0.00006953353,0.0001073438,0.0003478614,0.0005301289,0.00003157736,0.000006402558],"category_scores_gemma":[0.00002722457,0.00009487973,0.0000919772,0.0005266547,0.00004108694,0.0004110678,0.0004475302,0.00004112993,0.0004028636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000146517,"about_ca_system_score_gemma":0.00003186605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228369,"about_ca_topic_score_gemma":0.00002222485,"domain_scores_codex":[0.9989719,0.000008260881,0.0001671244,0.0003232193,0.0001705241,0.0003589728],"domain_scores_gemma":[0.9993154,0.0001883001,0.00003798665,0.0003121886,0.00006374977,0.00008238758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001618482,0.00001411712,0.000149115,0.00001217617,0.00001521656,0.000004912086,0.0001411664,0.000158214,0.00003792779,0.7921105,0.05833765,0.1490174],"study_design_scores_gemma":[0.0007983053,0.0001658555,0.002953409,0.00003690859,0.000007971974,0.0000189915,0.0001725969,0.5392402,0.004978041,0.4216835,0.02946997,0.0004742577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04511194,0.0000331964,0.846354,0.002545673,0.001072819,0.0004521331,0.000007691239,0.001448374,0.1029742],"genre_scores_gemma":[0.9532607,0.000001798726,0.03571076,0.0009421703,0.0001091278,0.00001173407,0.00002560473,0.00001597715,0.009922192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9081487,"threshold_uncertainty_score":0.5178131,"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."}}