{"id":"W1987918216","doi":"10.1002/mas.21360","title":"Mass spectrometry imaging under ambient conditions","year":2012,"lang":"en","type":"review","venue":"Mass Spectrometry Reviews","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":496,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Chemistry; Desorption electrospray ionization; Ambient ionization; Mass spectrometry; Analytical Chemistry (journal); Ionization; Direct electron ionization liquid chromatography–mass spectrometry interface; Matrix-assisted laser desorption electrospray ionization; Atmospheric-pressure laser ionization; Electrospray ionization; Mass spectrometry imaging; Ion source; Extractive electrospray ionization; Sample preparation in mass spectrometry; Chemical ionization; Thermal ionization mass spectrometry; Photoionization; Ion; Chromatography","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":[0.0009032062,0.001171036,0.0006172801,0.001533813,0.000671083,0.001281025,0.001133043,0.001676804,0.009511258],"category_scores_gemma":[0.001376028,0.0004725388,0.000462466,0.001058999,0.0007074003,0.002153525,0.001194775,0.001701712,0.007483888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005265205,"about_ca_system_score_gemma":0.0005924265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005898359,"about_ca_topic_score_gemma":0.0008655525,"domain_scores_codex":[0.9987684,0.0001315546,0.00005548823,0.0004727287,0.0004590195,0.0001128418],"domain_scores_gemma":[0.999421,0.0001252986,0.00008723973,0.00008876919,0.0002321727,0.00004556322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000123876,0.00002704127,0.0005513223,0.0003501097,0.00003578254,0.0003942749,0.0001047445,0.0001898515,0.9639661,0.002637441,0.005122085,0.02649731],"study_design_scores_gemma":[0.00004280955,0.0002410362,0.003970402,0.0001600446,0.00005909775,0.003213201,0.0001714351,0.006299024,0.8594771,0.003055746,0.1232219,0.00008829681],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1957114,0.03062742,0.6497893,0.003122254,0.002447469,0.0007453977,0.008844008,0.01763337,0.09107941],"genre_scores_gemma":[0.3964001,0.02675506,0.4986158,0.00333891,0.001233413,0.001244763,0.01194909,0.002708311,0.05775468],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.009511258,"threshold_uncertainty_score":0.03181833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05747144583369963,"score_gpt":0.3465692577572282,"score_spread":0.2890978119235286,"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."}}