{"id":"W2355603571","doi":"","title":"Canadian Electronic Evidence Act Breaking through Traditional Evidence Rules in Common Law System","year":2006,"lang":"en","type":"article","venue":"Hebei faxue","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hearsay; Federal Rules of Evidence; Legislation; Authentication (law); Law; Electronic records; Reliability (semiconductor); Rules of evidence; Business; Political science; Computer science; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01006261,0.0006576274,0.0006357545,0.004754446,0.01467121,0.01021805,0.003992134,0.007509369,0.01561577],"category_scores_gemma":[0.03963052,0.0008957253,0.0009087619,0.004620702,0.006249622,0.002920225,0.003122486,0.006399602,0.002908254],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0995744,"about_ca_system_score_gemma":0.2694193,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9778247,"about_ca_topic_score_gemma":0.9812675,"domain_scores_codex":[0.9662605,0.001857977,0.001603729,0.002554311,0.02483706,0.002886403],"domain_scores_gemma":[0.9521104,0.004723845,0.0008604335,0.002442932,0.03760765,0.002254745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003007091,0.00002870928,0.002183108,0.0001254275,0.00001549826,0.0003346618,0.00136297,0.0009332123,0.0004770814,0.7295887,0.2348413,0.03007924],"study_design_scores_gemma":[0.00003344677,0.00002160147,0.0072188,0.0003380489,0.00005467838,0.0001477059,0.000576539,0.00188111,0.0007715026,0.02756905,0.9612551,0.0001325315],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.016249,0.003467124,0.01599806,0.06344654,0.002040264,0.001100905,0.00421213,0.0006586331,0.8928274],"genre_scores_gemma":[0.2347269,0.005790382,0.05254432,0.05327641,0.0005093022,0.0007451577,0.003270777,0.0002517661,0.648885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9004256,"threshold_uncertainty_score":0.7224662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03209881255212452,"score_gpt":0.2320343890251664,"score_spread":0.1999355764730419,"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."}}