{"id":"W2804625960","doi":"10.1021/acs.iecr.8b00228","title":"Partial Least-Squares Regression as a Tool To Predict Fluoropolymer Surface Modification by Dielectric Barrier Discharge in a Corona Process Configuration in a Nitrogen–Organic Gaseous Precursor Environment","year":2018,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Electrohydrodynamics and Fluid Dynamics","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hôpital Saint-François d'Assise","funders":"","keywords":"Partial least squares regression; Contact angle; Dielectric barrier discharge; Nitrogen; Fluoropolymer; Corona discharge; Surface modification; Analytical Chemistry (journal); Ultraviolet; Plasma; Chemistry; Chemical engineering; Materials science; Dielectric; Composite material; Organic chemistry; Polymer; Optoelectronics; Electrode; Physical chemistry","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"],"consensus_categories":[],"category_scores_codex":[0.0006078484,0.0003198482,0.0002960924,0.0001889264,0.00008439203,0.00009319674,0.0003472726,0.000409167,0.0001631843],"category_scores_gemma":[0.0003587535,0.0003463481,0.00004317834,0.0009342352,0.00006172631,0.0001618706,0.00005913433,0.001049102,0.00004670005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008462791,"about_ca_system_score_gemma":0.0001684366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009366436,"about_ca_topic_score_gemma":0.00001133179,"domain_scores_codex":[0.9972947,0.00005688054,0.0005043685,0.0005293799,0.0006879265,0.000926747],"domain_scores_gemma":[0.9991826,0.00009120117,0.00003776068,0.0003745428,0.00007532817,0.0002385826],"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":[0.0001765725,0.00008836451,0.000621881,0.00006483858,0.00002068127,0.00001121827,0.000260773,0.03981783,0.9576971,0.00001590923,0.0001855055,0.001039361],"study_design_scores_gemma":[0.0006388047,0.0001204378,0.00003538729,0.00009979549,0.00000461574,0.000007107757,0.0000273265,0.4303636,0.5681963,0.00001791945,0.0002492341,0.0002394249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975596,0.0003243233,0.0007240652,0.00008592833,0.00009529028,0.0008296934,0.00007371516,0.0001283668,0.0001790169],"genre_scores_gemma":[0.9988692,0.00007393523,0.00001756524,0.00000285549,0.0002777196,0.0002538702,0.000181089,0.00008661559,0.0002371929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3905458,"threshold_uncertainty_score":0.9998989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01869134807513223,"score_gpt":0.2754655376178419,"score_spread":0.2567741895427097,"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."}}