{"id":"W2118159165","doi":"","title":"Efficient Indexing for Recursive Conditioning Algorithms","year":2010,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Search engine indexing; Computer science; Redundancy (engineering); Inference; Algorithm; Reduction (mathematics); Data mining; Theoretical computer science; Artificial intelligence; Mathematics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.004218646,0.000124602,0.0001275539,0.00004560584,0.001426271,0.0004679439,0.001474144,0.0001247168,0.00001617251],"category_scores_gemma":[0.0003126433,0.00009024009,0.0001702172,0.0005908826,0.000364416,0.0001476264,0.0004179417,0.001401041,0.000049237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006523015,"about_ca_system_score_gemma":0.0003070561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004607676,"about_ca_topic_score_gemma":0.000004640198,"domain_scores_codex":[0.9978087,0.0001287731,0.0001835232,0.0004300482,0.0007350459,0.0007139337],"domain_scores_gemma":[0.9974471,0.0009013592,0.00004759386,0.0006977696,0.0007559406,0.0001502727],"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":[0.00001730621,0.0001027761,0.00004776791,0.00004974517,0.00008506376,0.000004696038,0.01536438,0.004777108,0.02040945,0.8758358,0.0530896,0.03021633],"study_design_scores_gemma":[0.0002791859,0.00008136655,0.00009616443,0.00002614808,0.000003589787,0.00000904134,0.0003895018,0.9365363,0.004917875,0.05397487,0.003534463,0.0001515431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03387026,0.00005251935,0.9561465,0.007924407,0.0008969731,0.0004278911,0.00001057882,0.0001318421,0.0005390563],"genre_scores_gemma":[0.9042171,0.00001572153,0.09296367,0.0008589315,0.0009451139,0.0002499366,0.000007395819,0.00002000735,0.0007221108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9317591,"threshold_uncertainty_score":0.9998738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07135227563608171,"score_gpt":0.3889535313249901,"score_spread":0.3176012556889084,"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."}}