{"id":"W2009589847","doi":"10.1021/pr400710q","title":"“Out-Gel” Tryptic Digestion Procedure for Chemical Cross-Linking Studies with Mass Spectrometric Detection","year":2013,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"Western Economic Diversification Canada; Genome British Columbia; Genome Canada","keywords":"Chemistry; Chromatography; Bottom-up proteomics; Mass spectrometry; Digestion (alchemy); Gel electrophoresis; Peptide; Polyacrylamide gel electrophoresis; Two-dimensional gel electrophoresis; Peptide mass fingerprinting; Trypsin; Sample preparation; Proteolysis; Tandem mass spectrometry; Proteomics; Protein mass spectrometry; Biochemistry; Enzyme","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":[0.001217185,0.002214678,0.001422474,0.001110427,0.0007761577,0.0006891737,0.001282763,0.0009663472,0.009488796],"category_scores_gemma":[0.0009500196,0.0007349331,0.001044284,0.0008626961,0.0005183089,0.0008590184,0.0008038549,0.002906645,0.01189555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002666591,"about_ca_system_score_gemma":0.0007962753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003740275,"about_ca_topic_score_gemma":0.0008278866,"domain_scores_codex":[0.9985959,0.0002385671,0.0001719229,0.0004448265,0.0004011833,0.000147632],"domain_scores_gemma":[0.999198,0.0001267885,0.0001206426,0.0002665519,0.0002250029,0.00006301717],"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":[0.0001019037,0.00009903922,0.0001308761,0.0003261732,0.00002320638,0.0001169176,0.00003950814,0.00003858429,0.9900288,0.0002415369,0.00140753,0.007445889],"study_design_scores_gemma":[0.0000188148,0.0002289804,0.001679055,0.00005736211,0.00007139191,0.000680308,0.00002204347,0.000728026,0.9313279,0.0001954381,0.06495266,0.0000381795],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1225898,0.01011049,0.8273274,0.0007705087,0.002148771,0.004469815,0.008298812,0.009140942,0.01514351],"genre_scores_gemma":[0.1209683,0.01548718,0.7840757,0.001512816,0.0004648818,0.007646951,0.0318372,0.002193035,0.03581399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009488796,"threshold_uncertainty_score":0.03174317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0789299508568631,"score_gpt":0.4216341337756097,"score_spread":0.3427041829187465,"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."}}