{"id":"W7117257993","doi":"10.5267/j.ijiec.2025.12.002","title":"A general computational framework for precision quantification in heteroscedastic industrial data: theory, algorithms, and production control validation","year":2025,"lang":"","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heteroscedasticity; Range (aeronautics); Uncertainty quantification; Kriging; Metric (unit); Parametric statistics; Function (biology); Benchmark (surveying); Transformation (genetics); Probability density function","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005295331,0.0003856475,0.00072252,0.001945966,0.0002238519,0.0008718727,0.001474612,0.0004488481,0.00001293254],"category_scores_gemma":[0.05863378,0.0003940117,0.0001398491,0.001206664,0.0001649358,0.001710699,0.0002710488,0.001186213,0.000003057766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284582,"about_ca_system_score_gemma":0.0009085905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001174767,"about_ca_topic_score_gemma":0.000001961485,"domain_scores_codex":[0.9936925,0.0004281183,0.003032301,0.0008385209,0.001641114,0.0003674028],"domain_scores_gemma":[0.9811257,0.01389161,0.001568656,0.0003989932,0.00283527,0.0001798336],"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":[0.0008413616,0.0001963797,0.001013776,0.00001543197,0.0002524699,0.000007579021,0.0001535768,0.7717544,0.0001112936,0.02486787,0.0004748546,0.2003109],"study_design_scores_gemma":[0.004900719,0.0002004133,0.001665237,0.001512957,0.0001715511,0.00004211845,0.0001932232,0.8181707,0.0002117907,0.1713773,0.001255268,0.0002987548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01635615,0.0003864454,0.9575023,0.004019109,0.02024077,0.0009337462,0.000528316,0.00002581258,0.000007337246],"genre_scores_gemma":[0.8564823,0.00005958172,0.1396154,0.00005160763,0.003512829,0.0000312,0.0001569137,0.00003387242,0.00005634343],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8401262,"threshold_uncertainty_score":0.9998512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1543718229172454,"score_gpt":0.42930193833323,"score_spread":0.2749301154159847,"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."}}