{"id":"W2088864649","doi":"10.1198/00401700152672519","title":"A Profile-Based Approach to Parametric Sensitivity Analysis of Nonlinear Regression Models","year":2001,"lang":"en","type":"article","venue":"Technometrics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Parametric statistics; Nonlinear regression; Nonlinear system; Mathematics; Regression analysis; Parametric model; Measure (data warehouse); Statistics; Regression; Applied mathematics; Computer science; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.004339037,0.0002059486,0.0007203293,0.01367007,0.00007769096,0.00006730306,0.0006586826,0.0001938662,0.0000263269],"category_scores_gemma":[0.01253618,0.0001431115,0.0002782483,0.0944193,0.00008097193,0.0001372864,0.0001561526,0.0001978813,0.00002986874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002265,"about_ca_system_score_gemma":0.00007838749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002976061,"about_ca_topic_score_gemma":0.000001444736,"domain_scores_codex":[0.9964543,0.00009800494,0.0007165077,0.0006532471,0.001748746,0.0003292098],"domain_scores_gemma":[0.9956713,0.002031068,0.0002708509,0.00124386,0.0005946048,0.000188259],"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":[0.00003027573,0.0003580504,0.00397149,0.000009781445,0.00006387505,0.000007613781,0.000026127,0.971136,0.0001327972,0.001612167,0.0003935888,0.02225828],"study_design_scores_gemma":[0.0001574911,0.00006641679,0.003182519,0.000009875836,0.000164371,0.000002383281,0.00004477767,0.9941527,0.0004043047,0.001248131,0.0003795009,0.0001875205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09137366,0.0001299731,0.9055029,0.0000620589,0.00005422844,0.0003344523,0.00004166578,0.000159824,0.002341267],"genre_scores_gemma":[0.7882706,0.000008094722,0.2114164,0.00003283721,0.00001405815,0.00001716161,0.00001262981,0.00001235644,0.0002158687],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6968969,"threshold_uncertainty_score":0.9975091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1460250406753342,"score_gpt":0.3430785200124298,"score_spread":0.1970534793370955,"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."}}