{"id":"W1981952299","doi":"10.1016/s1044-0305(00)00200-2","title":"Application of an integrated matrix-assisted laser desorption/ionization time-of-flight, electrospray ionization mass spectrometry and tandem mass spectrometry approach to characterizing complex polyol mixtures","year":2001,"lang":"en","type":"article","venue":"Journal of the American Society for Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Chemistry; Mass spectrometry; Desorption electrospray ionization; Electrospray ionization; Polyol; Tandem mass spectrometry; Sample preparation in mass spectrometry; Electrospray; Capillary electrophoresis–mass spectrometry; Chromatography; Desorption; Ionization; Matrix-assisted laser desorption/ionization; Analytical Chemistry (journal); Organic chemistry; Chemical ionization; Polyurethane; Ion","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":[0.0005993833,0.000890175,0.0004314499,0.001425265,0.000416565,0.0006193403,0.0005866726,0.0006937026,0.0008351961],"category_scores_gemma":[0.001026486,0.0003396243,0.0002181948,0.0005656516,0.0005986519,0.0007577539,0.0005933261,0.0007566895,0.0005240229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003694288,"about_ca_system_score_gemma":0.0003383732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004200139,"about_ca_topic_score_gemma":0.000953277,"domain_scores_codex":[0.9994774,0.00006427977,0.00003082624,0.0001242292,0.0002760745,0.0000271055],"domain_scores_gemma":[0.9994809,0.0001563546,0.00009971111,0.00004831873,0.0001554848,0.00005918802],"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":[0.00005267975,0.00003416684,0.0005968977,0.00006170404,0.00001651386,0.00006445755,0.00002062583,0.000266142,0.9870112,0.0001997166,0.00007115732,0.01160479],"study_design_scores_gemma":[0.00002064026,0.0001820584,0.0046584,0.00000840368,0.00003666036,0.001002424,0.00002869516,0.01198422,0.9797106,0.0004649273,0.001872417,0.00003045909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4326896,0.004239795,0.5545,0.0004578317,0.0001120633,0.0005321597,0.0008529465,0.002181405,0.00443418],"genre_scores_gemma":[0.5505369,0.00295052,0.4426867,0.0003583332,0.00005319935,0.0004994979,0.0005129564,0.000100896,0.002300946],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001425265,"threshold_uncertainty_score":0.003169894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234016405163811,"score_gpt":0.2670269043628973,"score_spread":0.2546867403112592,"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."}}