{"id":"W4234519748","doi":"10.1515/iupac.85.0555","title":"Liquid Chromatography-Mass Spectrometry (LC-MS)","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada","funders":"","keywords":"Chemical nomenclature; Terminology; Mass spectrometry; Chemistry; Tandem mass spectrometry; Analytical Chemistry (journal); Chromatography; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003654833,0.001274889,0.00153624,0.000437691,0.0002547378,0.0001473883,0.001538254,0.001532647,0.07158508],"category_scores_gemma":[0.0004034926,0.001045401,0.00128513,0.0008627906,0.0006927722,0.0001504782,0.0003077481,0.001506334,0.00001780256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004172753,"about_ca_system_score_gemma":0.0006628536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005216756,"about_ca_topic_score_gemma":0.00004867354,"domain_scores_codex":[0.9940107,0.00004302999,0.001119147,0.001404965,0.002141328,0.00128084],"domain_scores_gemma":[0.9956916,0.0002258099,0.0006087922,0.002285552,0.0004583022,0.0007299209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004034846,0.0004479471,0.000034267,0.001371501,0.0008530717,0.0003156751,0.000003311395,2.459266e-7,0.01085196,0.00002124668,0.9856082,0.00008907836],"study_design_scores_gemma":[0.001235071,0.0001844868,0.000004012825,0.001122469,0.0005623577,0.00005462506,0.00003605863,0.000002242767,0.01840478,0.0003365965,0.9767404,0.00131691],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001283994,0.001809206,0.0002206557,0.0002674766,0.000291704,0.0001086553,0.9915032,0.000373556,0.004141492],"genre_scores_gemma":[0.0005884625,0.002206965,0.0001209974,0.0001920563,0.002768399,0.00002865477,0.9931126,0.0001330387,0.0008488796],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07156727,"threshold_uncertainty_score":0.9997635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008105729560878109,"score_gpt":0.3317607923010346,"score_spread":0.3236550627401565,"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."}}