{"id":"W2120343340","doi":"10.1002/rcm.4939","title":"Matrix‐free mass spectrometric imaging using laser desorption ionisation <scp>F</scp>ourier transform ion cyclotron resonance mass spectrometry","year":2011,"lang":"en","type":"article","venue":"Rapid Communications in Mass Spectrometry","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; University of Glasgow; Scottish Funding Council; Bill and Melinda Gates Foundation","keywords":"Chemistry; Fourier transform ion cyclotron resonance; Mass spectrometry; Mass spectrometry imaging; MALDI imaging; Matrix-assisted laser desorption/ionization; Analytical Chemistry (journal); Analyte; Matrix (chemical analysis); Sample preparation; Desorption electrospray ionization; Desorption; Chromatography; Ion; Ionization; Chemical ionization","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":[0.0008589518,0.001482673,0.0005559063,0.001433048,0.0006228571,0.0008725251,0.001020998,0.0008834595,0.0089144],"category_scores_gemma":[0.001162373,0.0004563318,0.0004133577,0.001021822,0.0008341832,0.001341564,0.0007179432,0.001151011,0.00322108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006296794,"about_ca_system_score_gemma":0.0005582559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009748125,"about_ca_topic_score_gemma":0.002045435,"domain_scores_codex":[0.9992068,0.00009746954,0.00005553621,0.000234465,0.0003416988,0.00006400279],"domain_scores_gemma":[0.9993961,0.0001815508,0.0001138298,0.00009036862,0.0001780681,0.00004006107],"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":[0.00007006386,0.00001643345,0.00009162353,0.0001111092,0.00001082857,0.0001086959,0.00002255751,0.00008047399,0.9864829,0.000372665,0.001078195,0.0115545],"study_design_scores_gemma":[0.00004135599,0.0001140553,0.001854014,0.00001725565,0.0000182717,0.001353044,0.00002246287,0.004009912,0.9777605,0.0004062325,0.01435075,0.00005212623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1471569,0.007920677,0.802319,0.001949616,0.000670563,0.0006519277,0.003292151,0.01069102,0.02534815],"genre_scores_gemma":[0.3080837,0.006274691,0.6599418,0.001104656,0.0002852199,0.001023903,0.002889314,0.001462572,0.01893412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0089144,"threshold_uncertainty_score":0.02982169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03077627736197315,"score_gpt":0.2820566376109268,"score_spread":0.2512803602489537,"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."}}