{"id":"W4225266676","doi":"10.1016/j.polymdegradstab.2022.109963","title":"Quantifying stabilizing additive hydrolysis and kinetics through principal component analysis of infrared spectra of cross-linked polyethylene pipe","year":2022,"lang":"en","type":"article","venue":"Polymer Degradation and Stability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinetics; Cross-linked polyethylene; Principal component analysis; Polyethylene; Hydrolysis; Infrared; Infrared spectroscopy; Chemistry; Materials science; Component (thermodynamics); Chemical engineering; Polymer chemistry; Organic chemistry; Biological system; Thermodynamics; Computer science; Optics; Physics; Engineering; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004963062,0.000490584,0.0002194038,0.0008039078,0.0001608018,0.0004160592,0.0001466997,0.0002636095,0.0005771495],"category_scores_gemma":[0.0007118586,0.0001639697,0.0002924153,0.0009576137,0.000238305,0.0003854044,0.0001722272,0.0005416832,0.0001938565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001700586,"about_ca_system_score_gemma":0.0002204484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001037408,"about_ca_topic_score_gemma":0.00107535,"domain_scores_codex":[0.9997792,0.00003034579,0.00001316653,0.0000606141,0.00009017644,0.00002654487],"domain_scores_gemma":[0.9996922,0.00008936474,0.00007135699,0.00002098804,0.0001082451,0.0000178353],"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":[0.00009386778,0.00004878382,0.002472157,0.00007723467,0.00001675676,0.00002616951,0.0001208907,0.001556086,0.9795836,0.0001067111,0.00007248479,0.01582534],"study_design_scores_gemma":[0.000008641922,0.0003035713,0.07400212,0.00001044194,0.00005376764,0.0001428266,0.0001558163,0.04243718,0.8811702,0.0002550667,0.001404686,0.00005572048],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9366605,0.000440761,0.06139743,0.00003509516,0.00002219067,0.00006384387,0.0003894305,0.000333792,0.0006569205],"genre_scores_gemma":[0.9544868,0.000788645,0.04283066,0.00002727826,0.00001029992,0.00009958617,0.0005300552,0.00008346056,0.001143117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001037408,"threshold_uncertainty_score":0.00262481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05289758669806226,"score_gpt":0.3201836208260656,"score_spread":0.2672860341280033,"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."}}