{"id":"W1486749294","doi":"10.1007/11766247_18","title":"Exploiting Dynamic Independence in a Static Conditioning Graph","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Overhead (engineering); Conditioning; Theoretical computer science; Graph; Inference; Node (physics); Computational complexity theory; Factor graph; Exponential function; Algorithm; Mathematics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002263835,0.001089157,0.001490912,0.00215894,0.001096206,0.001634053,0.002682371,0.001506916,0.009510917],"category_scores_gemma":[0.01529518,0.00164699,0.001453673,0.002736977,0.00215814,0.005365352,0.002609977,0.003026974,0.00109832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001299895,"about_ca_system_score_gemma":0.001488654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007160666,"about_ca_topic_score_gemma":0.008934842,"domain_scores_codex":[0.9986223,0.0004916853,0.00004958851,0.000344604,0.0003410521,0.0001506818],"domain_scores_gemma":[0.9848786,0.01174919,0.0005851671,0.001821488,0.00063612,0.0003294947],"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":[0.000289141,0.0001868541,0.001472708,0.0001724769,0.0001189043,0.0003095227,0.000244591,0.2935882,0.002990079,0.5902447,0.005683206,0.1046997],"study_design_scores_gemma":[0.00001966524,0.00001584359,0.0002257642,0.00001397845,0.00003338722,0.00006975111,0.00001233248,0.5889686,0.0007673569,0.4088506,0.001003853,0.00001894559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009964466,0.00007196879,0.9859735,0.0001793254,0.00002338127,0.00002524552,0.0001412059,0.0004731923,0.003147723],"genre_scores_gemma":[0.523046,0.0006253828,0.4631729,0.0004056616,0.0002415412,0.0001739069,0.001322337,0.000851266,0.01016084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009510917,"threshold_uncertainty_score":0.03181714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01722423899701606,"score_gpt":0.2528164388576222,"score_spread":0.2355921998606061,"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."}}