{"id":"W2749638919","doi":"10.3791/55933","title":"Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)","year":2017,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Warren Y. Soper Charitable Trust; Fondation De Famille Alvin Segal; Genome British Columbia; Genome Canada; McGill University Health Centre; Jewish General Hospital; Leading Edge Endowment Fund; McGill University","keywords":"Analyte; Chromatography; Mass spectrometry; Matrix-assisted laser desorption/ionization; Chemistry; Sample preparation; Reproducibility; Matrix (chemical analysis); Immunoassay; Proteomics; Peptide; MALDI imaging; Elution; Desorption; Biochemistry; Antibody; Adsorption; Biology","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":[0.001288029,0.001671003,0.0008773107,0.002242403,0.0004642183,0.0009719917,0.001478043,0.001142201,0.001508333],"category_scores_gemma":[0.001205542,0.0004278365,0.0006326012,0.001172925,0.0005865758,0.001238256,0.001238014,0.001193131,0.002004587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005385402,"about_ca_system_score_gemma":0.000404688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002964301,"about_ca_topic_score_gemma":0.0003837378,"domain_scores_codex":[0.9976012,0.0003270771,0.0001422948,0.0007156294,0.001062941,0.0001508557],"domain_scores_gemma":[0.9993284,0.0001227034,0.0001765073,0.0001003628,0.0002305778,0.00004142987],"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":[0.00005725365,0.00003680115,0.0004504736,0.0002262086,0.00003301106,0.00006787513,0.00003399301,0.0001565161,0.9755713,0.0004586834,0.0004101585,0.02249773],"study_design_scores_gemma":[0.000008176875,0.00010365,0.001772856,0.0000201915,0.0000385594,0.0005639695,0.00002101948,0.006170481,0.9825385,0.0003282319,0.008395519,0.00003883597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1483951,0.0163801,0.8181603,0.0005011011,0.0004275629,0.0003865675,0.002018267,0.005834872,0.007896163],"genre_scores_gemma":[0.2215427,0.009976279,0.7561523,0.0006981412,0.0001945456,0.0007971195,0.002621832,0.0004166983,0.007600388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002242403,"threshold_uncertainty_score":0.006811798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363341551026755,"score_gpt":0.4534150602404676,"score_spread":0.3997816447302,"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."}}