{"id":"W189866352","doi":"10.3233/978-1-60750-619-5-255","title":"Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks","year":2010,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Argument (complex analysis); Bayesian network; Probabilistic logic; Semantics (computer science); Computer science; Bayesian probability; Artificial intelligence; Natural language processing; 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":[0.0002679569,0.0002423836,0.0002871085,0.0001137976,0.0003803698,0.0002076464,0.0008067947,0.0002821193,0.00000242272],"category_scores_gemma":[0.00002584577,0.0001941467,0.0000927131,0.00008715832,0.0003684665,0.00009200334,0.0001558025,0.0003870957,0.000002291439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004804949,"about_ca_system_score_gemma":0.00009730179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001846141,"about_ca_topic_score_gemma":0.000149563,"domain_scores_codex":[0.9986076,0.000008328224,0.0004401029,0.0005158441,0.0001417598,0.0002863191],"domain_scores_gemma":[0.9988144,0.0001988099,0.0001733998,0.0006638964,0.00009056615,0.00005890965],"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":[0.000003889127,0.00002020034,0.000008795604,0.00002437647,0.00002013037,5.892437e-7,0.0001836015,0.0591443,0.000009504955,0.8022479,0.0001492297,0.1381875],"study_design_scores_gemma":[0.000006755997,0.000008249429,7.815898e-7,0.00002237736,0.00003011763,0.000001643711,0.00005783792,0.5434677,0.00006698328,0.4514733,0.004735766,0.0001285224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000006575001,0.001388368,0.994459,0.0007790697,0.0004554825,0.001376907,0.00001145612,0.00005313753,0.001469957],"genre_scores_gemma":[0.07407925,0.002592484,0.9092957,0.0005784524,0.001137986,0.001368835,0.00003599946,0.00009646453,0.0108148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4843234,"threshold_uncertainty_score":0.7917074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06489558896227246,"score_gpt":0.2892761182494512,"score_spread":0.2243805292871787,"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."}}