{"id":"W1822511634","doi":"","title":"Determining Good Elimination Orderings with Darwinian Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Heuristics; Heuristic; Bayesian network; Computer science; Darwinism; Artificial intelligence; Theoretical computer science; Machine learning; Biology; Genetics","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":[0.005746886,0.0009223889,0.0008963306,0.004132082,0.001867563,0.002351448,0.001351331,0.001317827,0.003907328],"category_scores_gemma":[0.03572793,0.0007450595,0.001277617,0.002434867,0.002540305,0.005862798,0.00245307,0.002326799,0.0004984306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002544277,"about_ca_system_score_gemma":0.002186892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003749471,"about_ca_topic_score_gemma":0.010211,"domain_scores_codex":[0.9955134,0.002439097,0.0002930055,0.0006009972,0.0008182666,0.0003351933],"domain_scores_gemma":[0.9723809,0.02225992,0.001212952,0.002043447,0.001498766,0.0006038671],"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":[0.0003862339,0.0001122314,0.008594432,0.0003583088,0.000155161,0.0002544042,0.0006082577,0.2808229,0.002352207,0.5793809,0.00478487,0.1221902],"study_design_scores_gemma":[0.00003433304,0.00002808575,0.0006886406,0.00004463896,0.00004144769,0.00008046492,0.0001138244,0.3549547,0.001963299,0.6383842,0.003633076,0.00003331673],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06250283,0.0003475442,0.9293616,0.0004832766,0.0000300302,0.0001027997,0.0003569012,0.0003921924,0.006422864],"genre_scores_gemma":[0.4770851,0.0004226313,0.5184896,0.0002464819,0.00005146312,0.0001916926,0.001028592,0.000229121,0.00225526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005746886,"threshold_uncertainty_score":0.03039283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184032835672841,"score_gpt":0.2365516302043392,"score_spread":0.2047113018476108,"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."}}