{"id":"W4238657552","doi":"10.1002/cpe.1405","title":"Distributed parallel compilation of MSBNs","year":2009,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Tree traversal; Asynchronous communication; Compiler; Set (abstract data type); Inference; Simple (philosophy); Distributed computing; Theoretical computer science; Parallel computing; Programming language; Artificial intelligence; Computer network","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":[0.001280294,0.0006302915,0.0005629433,0.0006974131,0.0005585428,0.001007032,0.001076815,0.000384774,0.005662166],"category_scores_gemma":[0.005027903,0.0004620289,0.0007337554,0.0006768874,0.0007287081,0.001176131,0.001453681,0.000828312,0.0009131604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007422224,"about_ca_system_score_gemma":0.00126829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003840887,"about_ca_topic_score_gemma":0.004733241,"domain_scores_codex":[0.9987071,0.000445656,0.00008939237,0.0002717748,0.0003661754,0.0001198622],"domain_scores_gemma":[0.9967816,0.001380839,0.0002030093,0.0009653229,0.0005435035,0.0001258148],"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":[0.0004701809,0.0002394888,0.002616482,0.0003513803,0.0001080985,0.0004130135,0.000680247,0.4542644,0.01759907,0.0631872,0.00768571,0.4523847],"study_design_scores_gemma":[0.00008864876,0.00006485094,0.0004150791,0.00003102441,0.00003310632,0.0001157911,0.00008499915,0.9318556,0.01119463,0.04518767,0.01091086,0.00001773237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03243279,0.0001192596,0.957099,0.0001112673,0.00004877337,0.0001147508,0.000116881,0.006473344,0.003483987],"genre_scores_gemma":[0.3457488,0.000119141,0.648913,0.00009689691,0.00003374938,0.0002319326,0.0004167405,0.0008303289,0.003609331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005662166,"threshold_uncertainty_score":0.01894188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0319058293536043,"score_gpt":0.3332848913637186,"score_spread":0.3013790620101143,"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."}}