{"id":"W2612443358","doi":"10.48550/arxiv.0906.5181","title":"Reasoning About a Simulated Printer Case Investigation with Forensic Lucid","year":2009,"lang":"en","type":"preprint","venue":"Spectrum Research Repository (Concordia University)","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Statement (logic); Context (archaeology); Event (particle physics); Witness; USable; Natural language processing; Automaton; Artificial intelligence; Sequence (biology); Programming language; Epistemology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000585185,0.0004860807,0.0005002969,0.00110612,0.0006965341,0.001232083,0.001913687,0.0003786553,0.000004030418],"category_scores_gemma":[0.00006207503,0.0004739594,0.0002316297,0.001662932,0.0007170463,0.001090095,0.002608053,0.002018624,0.00003047846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009085211,"about_ca_system_score_gemma":0.001560624,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008696271,"about_ca_topic_score_gemma":0.004198082,"domain_scores_codex":[0.99551,0.0005373948,0.0003494625,0.001504868,0.001055787,0.001042505],"domain_scores_gemma":[0.9964625,0.000175978,0.000288356,0.001805537,0.0006376125,0.0006300106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"case_report","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001389205,0.0007709513,0.08505034,0.0008764928,0.001779204,0.4191682,0.006766135,0.0164997,0.001178286,0.4090294,0.006450436,0.05104162],"study_design_scores_gemma":[0.01170725,0.01062918,0.159401,0.0102441,0.0007694691,0.03448892,0.003368401,0.3027894,0.1437573,0.2477813,0.06317502,0.01188859],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9022541,0.0000874875,0.01001083,0.0008234279,0.0004846794,0.0007601453,0.00000866495,0.0005151189,0.08505554],"genre_scores_gemma":[0.9907923,0.00002146854,0.001671551,0.00004947874,0.0002210517,0.000002422692,0.00002293532,0.00003660802,0.007182168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3846793,"threshold_uncertainty_score":0.9998047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02546382936179733,"score_gpt":0.251345691731141,"score_spread":0.2258818623693437,"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."}}