{"id":"W1978280947","doi":"10.1016/j.chroma.2012.07.092","title":"Application of modern reversed-phase peptide retention prediction algorithms to the Houghten and DeGraw dataset: Peptide helicity and its effect on prediction accuracy","year":2012,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Research Manitoba","funders":"Pacific Northwest National Laboratory; Natural Sciences and Engineering Research Council of Canada","keywords":"Peptide; Chemistry; Outlier; High-performance liquid chromatography; Amino acid; Chromatography; Artificial intelligence; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005628597,0.000118176,0.0001883816,0.00009603891,0.00007803597,0.00001818232,0.00009522997,0.00008829102,0.00000237013],"category_scores_gemma":[0.0001263956,0.00007941917,0.0001419276,0.0001593311,0.00003632138,0.00003010523,0.00004041749,0.00009576233,5.571919e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008965887,"about_ca_system_score_gemma":0.000007495908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007994065,"about_ca_topic_score_gemma":0.000002881621,"domain_scores_codex":[0.9991023,0.00008459357,0.0003228241,0.0001555212,0.0002076338,0.0001271183],"domain_scores_gemma":[0.9992414,0.00004649675,0.000322313,0.0001818595,0.00008121858,0.0001267676],"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":[0.0002341175,0.0001490104,0.008464291,0.00004725405,0.0001821078,3.463009e-7,0.00005692569,0.00002347063,0.9669525,0.000005339138,0.001859656,0.02202498],"study_design_scores_gemma":[0.001661672,0.001747968,0.05625419,0.0001749785,0.0006092779,0.0001265225,0.00006800282,0.002967797,0.9213077,0.000062877,0.01482862,0.0001904064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913885,0.001494806,0.006391378,0.0001185132,0.00004039679,0.0001876249,0.0003567958,0.000003809841,0.00001815743],"genre_scores_gemma":[0.9985708,0.000607223,0.0002509338,0.00006230419,0.0003135811,0.00001223082,0.000170214,0.000009103137,0.000003597293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04778991,"threshold_uncertainty_score":0.323862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029291190246502,"score_gpt":0.2663269895428022,"score_spread":0.2560340776403371,"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."}}