{"id":"W2147679417","doi":"10.1109/iembs.2005.1615452","title":"A novel approach to speed up peptide sequencing via MS/MS spectra analysis","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Chromatography; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008533359,0.001074219,0.0008648547,0.001120272,0.000789942,0.0009551006,0.001499254,0.0009080561,0.003324592],"category_scores_gemma":[0.001463721,0.0005598144,0.0006508962,0.001215676,0.000525402,0.00183564,0.00118742,0.001348643,0.001503806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004698604,"about_ca_system_score_gemma":0.001081495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120537,"about_ca_topic_score_gemma":0.002117028,"domain_scores_codex":[0.9993098,0.00008622978,0.00004453583,0.000165452,0.0003359508,0.00005809985],"domain_scores_gemma":[0.9990614,0.0002895454,0.00007300984,0.0002412948,0.0002656005,0.00006917853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005299665,0.0002688989,0.0008860719,0.0002843109,0.00009912987,0.0002757557,0.0001254348,0.02328252,0.4371912,0.01693033,0.005733056,0.5143934],"study_design_scores_gemma":[0.0002280185,0.0004927501,0.001292841,0.00002450537,0.00007396805,0.001278648,0.00004365209,0.7106808,0.2179767,0.01984335,0.04796069,0.0001040666],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01046433,0.0003751239,0.9844331,0.0001860005,0.0001631821,0.0001343743,0.00009091553,0.003066533,0.001086519],"genre_scores_gemma":[0.01874273,0.0001425405,0.9796097,0.00008615969,0.00004779146,0.00007408421,0.0001161277,0.0001096438,0.001071243],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003324592,"threshold_uncertainty_score":0.01112187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515619008736522,"score_gpt":0.2800798438306157,"score_spread":0.2549236537432505,"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."}}