{"id":"W2905287168","doi":"10.1021/acs.macromol.8b01943","title":"Detection of PLP Structure for Accurate Determination of Propagation Rate Coefficients over an Enhanced Range of PLP-SEC Conditions","year":2018,"lang":"en","type":"article","venue":"Macromolecules","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Federal Agency for Scientific Organizations; Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky","keywords":"Polymerization; Chemistry; Range (aeronautics); Molar mass; Biological system; Kinetic energy; Measure (data warehouse); Analytical Chemistry (journal); Noise (video); Computational physics; Statistical physics; Molecular physics; Thermodynamics; Chromatography; Polymer; Materials science; Physics; Computer science; Organic chemistry; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024925,0.0004996367,0.0002505162,0.0003631609,0.0002016613,0.0003661734,0.0003256031,0.0003101669,0.001037619],"category_scores_gemma":[0.003030986,0.0002503996,0.0001302753,0.0003640387,0.0004251978,0.0007304894,0.0002842013,0.0008321124,0.0003630913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003423746,"about_ca_system_score_gemma":0.0003444384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003114654,"about_ca_topic_score_gemma":0.0003895693,"domain_scores_codex":[0.9996843,0.00006011359,0.00002111456,0.00008579136,0.000128451,0.0000202744],"domain_scores_gemma":[0.998592,0.0008733091,0.0002478253,0.0001284952,0.000130488,0.00002786621],"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":[0.00009137352,0.00003268963,0.001820321,0.00006855861,0.000005665322,0.00005437876,0.00006214178,0.003730287,0.9840651,0.00109284,0.0000455012,0.008931119],"study_design_scores_gemma":[0.000004786704,0.00009298026,0.002705765,0.000005325968,0.000005450326,0.00007523828,0.00001585482,0.05553889,0.9406069,0.0003761616,0.000563056,0.000009505662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6672317,0.0004978432,0.3296491,0.0001011835,0.00001967373,0.00008892344,0.0002062363,0.0006579131,0.001547445],"genre_scores_gemma":[0.9226469,0.0004600558,0.07610744,0.00003126998,0.000008222463,0.0001079931,0.0001336833,0.00005850181,0.0004458368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001037619,"threshold_uncertainty_score":0.005420446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01031903038604397,"score_gpt":0.2898156197522135,"score_spread":0.2794965893661695,"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."}}