{"id":"W198834749","doi":"10.1007/978-3-7908-1792-8_18","title":"Computational Inference for Evidential Reasoning in Support of Judicial Proof","year":2002,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Argumentation theory; Computer science; Relevance (law); Context (archaeology); Evidential reasoning approach; Artificial intelligence; Argumentation framework; Deductive reasoning; Defeasible reasoning; Process (computing); Management science; Epistemology; Data science; Decision support system; Political science; Business decision mapping; Law; Engineering; Programming language","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":[0.001121839,0.0002192023,0.0006530142,0.0002063132,0.0003791965,0.00003564466,0.0002112826,0.0001966282,0.00003975372],"category_scores_gemma":[0.00128054,0.0002385217,0.00008135509,0.0001114907,0.001135331,0.0001109493,0.0002291951,0.0002522679,0.000002236708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321145,"about_ca_system_score_gemma":0.0001601643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003690277,"about_ca_topic_score_gemma":0.002414565,"domain_scores_codex":[0.9980482,0.0000507188,0.0007868116,0.00038589,0.0003877702,0.0003405997],"domain_scores_gemma":[0.997508,0.001533044,0.0003921265,0.00008841338,0.0004367545,0.00004165309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005115422,0.00005578646,0.008466378,0.0004535542,0.0001008409,0.00002630448,0.04438739,0.007460222,7.988172e-7,0.8765936,0.0007277792,0.06167613],"study_design_scores_gemma":[0.001168616,0.0004620713,0.001609253,0.007957902,0.0001778119,0.000005869754,0.01344969,0.03207475,0.00002051393,0.8896335,0.05140742,0.002032614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05006312,0.01451496,0.05113511,0.003082801,0.01050107,0.007865869,0.0001982633,0.0003167132,0.8623221],"genre_scores_gemma":[0.993376,0.0004837451,0.0016813,0.00006930128,0.0007574559,0.00001779056,0.000009870472,0.00002743061,0.003577061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9433129,"threshold_uncertainty_score":0.972663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1437173194789029,"score_gpt":0.4202371468425699,"score_spread":0.276519827363667,"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."}}