{"id":"W1882220678","doi":"10.48550/arxiv.1301.7408","title":"Context-Specific Approximation in Probabilistic Inference","year":2013,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Probabilistic logic; Inference; Exploit; Computer science; Bayesian network; Context (archaeology); Probabilistic relevance model; Theoretical computer science; Divergence-from-randomness model; Probabilistic argumentation; Decomposition; Bayesian inference; Approximate inference; Probabilistic analysis of algorithms; Bayesian probability; Artificial intelligence","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":[0.0001319476,0.0001234167,0.0001301524,0.000135967,0.00006980954,0.0001221885,0.0007028513,0.00007113138,0.00005144063],"category_scores_gemma":[0.00003906336,0.0001314455,0.00003586169,0.0006570203,0.00007070693,0.0009658893,0.0001464262,0.0001625995,0.0004304601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008043606,"about_ca_system_score_gemma":0.00006931121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002059843,"about_ca_topic_score_gemma":0.00004663317,"domain_scores_codex":[0.9989991,0.00007182322,0.0001491505,0.0004706782,0.0000570765,0.0002521987],"domain_scores_gemma":[0.9991577,0.00009322369,0.00006231292,0.0004539695,0.0001374364,0.0000954096],"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":[0.000003223882,0.00006402148,0.002319606,0.000009676747,0.000002929296,0.00001647463,0.0002388972,0.01443029,0.0001490438,0.971684,0.0001160048,0.01096586],"study_design_scores_gemma":[0.0002524477,0.00003255537,0.004632078,0.00002603376,0.000001630651,0.000001613099,0.00006251332,0.8108647,0.00006639068,0.1837867,0.0001042343,0.0001691521],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3108289,0.00002122267,0.6869833,0.0001153819,0.00006166918,0.0001605379,3.936279e-7,0.0001027747,0.001725831],"genre_scores_gemma":[0.9977552,0.00002711919,0.001769572,0.00009738901,0.000011546,0.000002598335,0.000001505175,0.000004678748,0.0003304299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7964343,"threshold_uncertainty_score":0.5532837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08344594726381369,"score_gpt":0.1840813672591115,"score_spread":0.1006354199952978,"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."}}