{"id":"W4312738503","doi":"10.1007/978-3-031-21244-4_24","title":"A Probabilistic Approach to Analyzing Agent Relations in Three-Way Conflict Analysis Based on Bayesian Confirmation","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Probabilistic logic; Alliance; Function (biology); Bayesian probability; Set (abstract data type); Binary relation; Neutrality; Semantics (computer science); Construct (python library); Relation (database); Conflict analysis; Artificial intelligence; Data mining; Conflict resolution; Mathematics; Political science","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"],"consensus_categories":[],"category_scores_codex":[0.001673785,0.0005246251,0.0006937734,0.003406912,0.000366303,0.0005665941,0.002521237,0.0002314469,0.00006591795],"category_scores_gemma":[0.0001562896,0.0005193212,0.0002229143,0.003967474,0.0002240113,0.0003680357,0.0006117198,0.001103377,0.00002354565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000817002,"about_ca_system_score_gemma":0.0006016732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001540416,"about_ca_topic_score_gemma":0.0003646246,"domain_scores_codex":[0.9952981,0.0001240926,0.0007887916,0.001921606,0.001236786,0.0006306348],"domain_scores_gemma":[0.9970829,0.0005397552,0.000305643,0.001629096,0.000196564,0.0002460374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006151213,0.0000654334,0.0001828678,0.00001744958,0.00002013911,0.00001523874,0.0007039198,0.9057456,0.000005766007,0.05309574,0.000004450654,0.04013723],"study_design_scores_gemma":[0.0001777217,0.0001515851,0.0008141213,0.0001323761,0.00004397955,0.000004504319,2.620942e-7,0.9641755,0.00001336155,0.03376749,0.0001741763,0.0005449522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009458222,0.00006309156,0.990373,0.00105623,0.0003341916,0.0007113552,0.00001157668,0.0001407198,0.007215281],"genre_scores_gemma":[0.7293065,0.000003335875,0.2691366,0.001285434,0.0000632883,0.0000723183,0.00003362728,0.00002296675,0.00007597578],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7292119,"threshold_uncertainty_score":0.9997258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03675140658481522,"score_gpt":0.2602073970714563,"score_spread":0.2234559904866411,"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."}}