{"id":"W7027250905","doi":"","title":"Circuit uncertainty quantification in time domain using sensitivity integrated stochastic collocation method","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Uncertainty quantification; Sensitivity (control systems); Collocation (remote sensing); Time domain; Uncertainty analysis; Domain (mathematical analysis); Measurement uncertainty","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01110488,0.0008857482,0.001434428,0.001191628,0.0007247429,0.0003639079,0.001155472,0.001057772,0.0001643645],"category_scores_gemma":[0.01908165,0.0008432448,0.0003629859,0.003796116,0.00008859659,0.0007267059,0.0001310072,0.001782624,0.0007058362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832078,"about_ca_system_score_gemma":0.0003696138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005837919,"about_ca_topic_score_gemma":0.0007510419,"domain_scores_codex":[0.9897888,0.002744013,0.002151317,0.002121769,0.002452013,0.0007420937],"domain_scores_gemma":[0.9923216,0.003632976,0.00118053,0.001184238,0.00127719,0.0004035411],"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":[0.0006451788,0.0004081711,0.00001788176,0.0002324409,0.0002011565,0.0001612815,0.0001591027,0.476166,0.3364246,0.08667789,0.00002195114,0.0988844],"study_design_scores_gemma":[0.002267863,0.0002624389,0.002125649,0.001663386,0.0004862911,0.0001222186,0.002115836,0.7153468,0.01914177,0.2480396,0.004892674,0.003535437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9214705,0.0001604563,0.06325305,0.0000712028,0.002914877,0.003370058,0.001872076,0.0006973273,0.006190508],"genre_scores_gemma":[0.9787843,0.000007241516,0.01767227,0.00008800386,0.00005409084,0.00009660958,0.001305608,0.0001601726,0.001831667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3172828,"threshold_uncertainty_score":0.9994018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08126894715687018,"score_gpt":0.326778540295414,"score_spread":0.2455095931385438,"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."}}