{"id":"W4292865343","doi":"10.1007/s00216-022-04262-6","title":"Absolute quantification of cholesterol from thin tissue sections by silver-assisted laser desorption ionization mass spectrometry imaging","year":2022,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Institute of Neurosciences, Mental Health and Addiction; Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrometry imaging; Mass spectrometry; Chemistry; Cholesterol; Quantitative analysis (chemistry); Surface-enhanced laser desorption/ionization; Analytical Chemistry (journal); Chromatography; Tandem mass spectrometry; Protein mass spectrometry; Biochemistry","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001843784,0.0002491261,0.0003474756,0.00008820742,0.0002991313,0.00008162825,0.0002888588,0.0001405003,0.009474957],"category_scores_gemma":[0.00007425922,0.0002566608,0.0001374611,0.0008610582,0.0002182471,0.0001011152,0.0001565426,0.0004920334,0.000008504413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001920002,"about_ca_system_score_gemma":0.00003651273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001903501,"about_ca_topic_score_gemma":0.000003583337,"domain_scores_codex":[0.9979463,0.00002822843,0.0005909203,0.0006430604,0.0004759641,0.0003155439],"domain_scores_gemma":[0.9988337,0.0001453173,0.0002274526,0.0004924274,0.00009669876,0.0002043886],"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":[0.00003424946,0.0004146986,0.003391564,0.00008533541,0.0001022277,0.000003747404,0.00001206407,0.00003723229,0.9919133,0.001277384,0.00153331,0.001194858],"study_design_scores_gemma":[0.0005786811,0.00003802096,0.00191613,0.00003028418,0.0004100987,0.00003501965,0.000314102,0.06842597,0.914242,0.004431346,0.009080723,0.0004976187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9390327,0.0005800885,0.03157704,0.003946078,0.00005348799,0.0003086832,0.001648609,0.0004676211,0.02238571],"genre_scores_gemma":[0.9947948,0.00006958628,0.001145032,0.0000818869,0.0001069783,0.00006837974,0.001047942,0.00002867025,0.002656729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07767132,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144291723855253,"score_gpt":0.2498854262669286,"score_spread":0.238442509028376,"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."}}