{"id":"W1995408105","doi":"10.1016/s1044-0305(01)00308-7","title":"Laser desorption ionization and MALDI time-of-flight mass spectrometry for low molecular mass polyethylene analysis","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":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Standards and Technology; U.S. Department of Commerce","keywords":"Chemistry; Polyethylene; Mass spectrometry; Analytical Chemistry (journal); Mass spectrum; Desorption; Desorption electrospray ionization; Matrix-assisted laser desorption/ionization; Ionization; Sample preparation in mass spectrometry; Ambient ionization; Matrix-assisted laser desorption electrospray ionization; Soft laser desorption; Ion; Chemical ionization; Chromatography; Electrospray ionization; Organic chemistry; Adsorption","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":[0.001137012,0.0009995197,0.0005912751,0.001480489,0.0004686507,0.0004840889,0.0007539098,0.0005668044,0.005415258],"category_scores_gemma":[0.00134591,0.0003161354,0.0002816678,0.00101227,0.0003593143,0.0008856255,0.0006590428,0.001250399,0.003714899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347257,"about_ca_system_score_gemma":0.0003739253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002346666,"about_ca_topic_score_gemma":0.0006247921,"domain_scores_codex":[0.9993877,0.000130222,0.00004647353,0.0001128459,0.0002769983,0.00004565931],"domain_scores_gemma":[0.9994923,0.0001755994,0.00006559663,0.00007586937,0.000123756,0.00006682346],"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":[0.0001398862,0.00005048838,0.0003551009,0.0002219215,0.00001334328,0.0000909553,0.00003266908,0.0001015743,0.9684544,0.0005390416,0.0006698208,0.02933063],"study_design_scores_gemma":[0.00005747184,0.0004501539,0.005093112,0.00003213092,0.00003590362,0.00251457,0.00006839024,0.005320701,0.9549696,0.00130057,0.03010303,0.0000543563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905584,0.01926593,0.7684347,0.00124234,0.0005738957,0.000876253,0.004248599,0.003763167,0.01103669],"genre_scores_gemma":[0.1931715,0.01077266,0.7799983,0.0004759404,0.0001389383,0.001019719,0.004281697,0.0004164434,0.009724848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005415258,"threshold_uncertainty_score":0.01811588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006557884428246998,"score_gpt":0.2509217061317238,"score_spread":0.2443638217034768,"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."}}