{"id":"W2617940263","doi":"10.1039/c6an02510b","title":"Comparison of peptide retention prediction algorithm in reversed-phase chromatography. Comment on “Predictive chromatography of peptides and proteins as a complementary tool for proteomics”, by I. A. Tarasova, C. D. Masselon, A. V. Gorshkov and M. V. Gorshkov, Analyst, 2016, <b>141</b>, 4816","year":2017,"lang":"en","type":"article","venue":"The Analyst","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Proteomics; Chromatography; Peptide; Chemistry; Reversed-phase chromatography; High-performance liquid chromatography; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007566388,0.0004916293,0.00106122,0.0002914297,0.0005799881,0.0001035542,0.000568922,0.0002343749,0.00005927356],"category_scores_gemma":[0.00008657923,0.0004103605,0.0004763526,0.0003756856,0.0007530386,0.0002824769,0.0001876002,0.0003846063,0.000001044451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006236223,"about_ca_system_score_gemma":0.00005208542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167677,"about_ca_topic_score_gemma":0.00007512376,"domain_scores_codex":[0.9969003,0.0001036963,0.001260328,0.0007192328,0.0005510544,0.0004653391],"domain_scores_gemma":[0.9970583,0.0001677604,0.001338476,0.00106405,0.0001960959,0.0001752573],"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":[0.002723705,0.004059606,0.4255019,0.003231711,0.004694805,0.00001840841,0.001681615,0.00004686792,0.5188927,0.0006904725,0.03558148,0.002876686],"study_design_scores_gemma":[0.01657696,0.003108646,0.02901535,0.003198819,0.003574602,0.00004066475,0.01230832,0.03695743,0.8851001,0.004268778,0.004313221,0.001537144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915823,0.0005240131,0.003016697,0.000814032,0.00002956987,0.001179446,0.002091367,0.00005457643,0.000707962],"genre_scores_gemma":[0.9961344,0.0002659372,0.002337908,0.00007391572,0.00008260447,0.0002748146,0.000706716,0.0000397827,0.00008389513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3964866,"threshold_uncertainty_score":0.9998348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877414286063082,"score_gpt":0.3057568239325828,"score_spread":0.286982681071952,"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."}}