{"id":"W2091914257","doi":"10.1021/pr800986c","title":"Automated 2D Peptide Separation on a 1D Nano-LC-MS System","year":2009,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Ontario Institute for Cancer Research","funders":"National Cancer Institute; Canadian Institutes of Health Research","keywords":"Proteome; Chromatography; Mass spectrometry; Peptide; Resolution (logic); Proteomics; Chemistry; High resolution; Instrumentation (computer programming); Chromatographic separation; High-performance liquid chromatography; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002375,0.001415947,0.001791223,0.001684277,0.001062346,0.001217668,0.001665879,0.001303807,0.01111069],"category_scores_gemma":[0.0008271194,0.00083983,0.0006223067,0.0006513319,0.0004491899,0.001105497,0.001112073,0.00134252,0.006042975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007773464,"about_ca_system_score_gemma":0.00136913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008946927,"about_ca_topic_score_gemma":0.001816518,"domain_scores_codex":[0.9986535,0.0001308079,0.0001360116,0.0004025563,0.0005700646,0.000107138],"domain_scores_gemma":[0.9995003,0.0001524381,0.00003648167,0.00007042509,0.0001600553,0.00008041248],"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":[0.0005827684,0.0003006175,0.0009883617,0.0003701811,0.00008404507,0.0001480323,0.00005277579,0.001142247,0.9575055,0.0008372871,0.006523495,0.03146463],"study_design_scores_gemma":[0.0004168012,0.0009230891,0.007483828,0.00008812904,0.0001523318,0.001541058,0.00007239963,0.123219,0.7881349,0.002415715,0.07514784,0.0004049288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1850411,0.003879499,0.7184594,0.001523011,0.001349091,0.003929345,0.02327702,0.05165653,0.01088511],"genre_scores_gemma":[0.1784032,0.002170835,0.7803422,0.003433544,0.000393507,0.007158644,0.01608824,0.001835398,0.0101744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01111069,"threshold_uncertainty_score":0.03716898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04875135099176469,"score_gpt":0.4204207985177487,"score_spread":0.371669447525984,"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."}}