{"id":"W2033422100","doi":"10.1016/j.jasms.2004.05.011","title":"Temperature-programmed pyrolysis hyphenated with metastable atom bombardment ionization mass spectrometry (TPPy/MAB-MS) for the identification of additives in polymers","year":2004,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Chemistry; Mass spectrometry; Fragmentation (computing); Ionization; Polymer; Fast atom bombardment; Analytical Chemistry (journal); Ion; Chemical ionization; Metastability; Ion source; Thermal ionization; Electron ionization; Chromatography; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000972577,0.000360748,0.0007697624,0.0002926667,0.0003784286,0.0001328817,0.0009834267,0.0001163324,0.000250205],"category_scores_gemma":[0.0001516378,0.0002296548,0.001241348,0.003787456,0.0004688986,0.0002268122,0.00005850239,0.0005695162,5.00132e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000928463,"about_ca_system_score_gemma":0.0002529014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000941138,"about_ca_topic_score_gemma":0.00001067699,"domain_scores_codex":[0.9970479,0.00004092656,0.001150521,0.0004040657,0.0007881733,0.0005684177],"domain_scores_gemma":[0.9955396,0.0004104385,0.002773808,0.0007170575,0.0004488239,0.0001102466],"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":[0.0003098319,0.0004138408,0.001419506,0.0001198442,0.001225147,9.496496e-7,0.0002409874,0.000655633,0.9895186,0.004937465,0.0005191067,0.0006390171],"study_design_scores_gemma":[0.002542696,0.0006051869,0.003688979,0.0001294431,0.0009078091,0.00004079207,0.006083161,0.0003888234,0.9736681,0.009843842,0.001696704,0.000404466],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7693301,0.001549581,0.2185833,0.00780306,0.0001409547,0.001551166,0.0005475312,0.0001072508,0.0003869774],"genre_scores_gemma":[0.8913556,0.0005220444,0.1072223,0.0001478467,0.0002129653,0.0002146294,0.00004436565,0.0000708999,0.0002092794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1220255,"threshold_uncertainty_score":0.9365051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007438832546451561,"score_gpt":0.2479453822516055,"score_spread":0.2405065497051539,"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."}}