{"id":"W2045325565","doi":"10.1366/000370202760354713","title":"In-Line Monitoring of Polymer Processing. II: Spectral Data Analysis","year":2002,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Partial least squares regression; Principal component analysis; Principal component regression; Spectral line; Biological system; Multiplicative function; Analytical Chemistry (journal); Spectrometer; Chemistry; Mathematics; Materials science; Optics; Statistics; Physics; Chromatography; Mathematical analysis","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001413008,0.0003362574,0.0007788051,0.0006309098,0.000134027,0.00004362567,0.001014617,0.0001690923,0.006881127],"category_scores_gemma":[0.0000282259,0.0003372548,0.0001462477,0.0035439,0.0001342949,0.0002249347,0.0002700334,0.000408987,0.00003803388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332729,"about_ca_system_score_gemma":0.00003185848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001388935,"about_ca_topic_score_gemma":0.00002349568,"domain_scores_codex":[0.997457,0.000007425015,0.0006323557,0.0008193321,0.0004612612,0.0006226698],"domain_scores_gemma":[0.9981555,0.00005707356,0.0002902259,0.001342695,0.00003197297,0.0001225567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006399564,0.0006970252,0.03521626,0.00009941908,0.0006627635,0.00001522521,0.0003384796,0.00006727688,0.9612055,0.0006557201,0.0004415803,0.0005366828],"study_design_scores_gemma":[0.0005878119,0.00003113259,0.0007142738,0.00001284015,0.001061813,0.0000027191,0.000312494,0.001767734,0.9947613,0.0002333432,0.0001885538,0.0003259621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9149534,0.006262683,0.001484265,0.0003019519,0.00006025235,0.00009795418,0.00007142705,0.0001512596,0.07661682],"genre_scores_gemma":[0.9923236,0.0003138034,0.004222317,0.00004310995,0.0004149413,0.00001701451,0.00006313514,0.00003968191,0.002562365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07737025,"threshold_uncertainty_score":0.999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04079423446206292,"score_gpt":0.3083071632250463,"score_spread":0.2675129287629834,"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."}}