{"id":"W7052240118","doi":"","title":"A random-discretization based Monte Carlo method for numerical integration","year":2003,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Electrostatic Discharge in Electronics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Histogram; Normal distribution; Distribution (mathematics); Monte Carlo method; Variance-gamma distribution; Transformation (genetics); Log-normal distribution","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001415003,0.000709781,0.0008766956,0.001113242,0.0007279977,0.001420022,0.001843062,0.001116987,0.05395836],"category_scores_gemma":[0.008187933,0.0005373558,0.0009280253,0.001595901,0.000363471,0.0009654987,0.0009385832,0.001841061,0.01289444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000877016,"about_ca_system_score_gemma":0.00177066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004380543,"about_ca_topic_score_gemma":0.006245013,"domain_scores_codex":[0.9988043,0.0004346911,0.00007086304,0.0001269368,0.0005154844,0.00004778168],"domain_scores_gemma":[0.9977907,0.001097734,0.00007974061,0.000319152,0.0006594996,0.00005318687],"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":[0.0001190699,0.0001109596,0.001067187,0.0004492319,0.0001013402,0.0001435159,0.00009317238,0.4469144,0.002785891,0.190028,0.06121764,0.2969695],"study_design_scores_gemma":[0.00002810833,0.00002096211,0.0001982611,0.00004596479,0.00001180708,0.00004912339,0.00001174243,0.9359977,0.001032118,0.02865865,0.03392393,0.00002163208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000788213,0.0003603233,0.9858511,0.000115015,0.0002272498,0.0001466089,0.000748004,0.001919787,0.009843671],"genre_scores_gemma":[0.02618207,0.0003911883,0.9571611,0.0001425355,0.00007349958,0.0007826329,0.001437915,0.001096872,0.01273212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05395836,"threshold_uncertainty_score":0.1805087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007110302383631795,"score_gpt":0.2162446707298988,"score_spread":0.209134368346267,"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."}}