{"id":"W2057762488","doi":"10.1021/ac051639u","title":"Correlation and Convolution Analysis of Peptide Mass Spectra","year":2006,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chemistry; Mass spectrometry; Convolution (computer science); Peptide; Biological system; Tandem mass spectrometry; Mass spectrum; Noise (video); Ion; Proteomics; Algorithm; Computational biology; Analytical Chemistry (journal); Computer science; Chromatography; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002064203,0.0007025687,0.0005957158,0.001934552,0.0005313195,0.0011606,0.0008230006,0.0005746473,0.003410426],"category_scores_gemma":[0.005511827,0.0002876593,0.000949309,0.001979308,0.000661963,0.001516362,0.001132606,0.0006703026,0.001444059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005719024,"about_ca_system_score_gemma":0.001416091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001914944,"about_ca_topic_score_gemma":0.001793529,"domain_scores_codex":[0.9988264,0.000224362,0.00007484259,0.0002864969,0.0004430124,0.0001447299],"domain_scores_gemma":[0.9978668,0.0009575317,0.0002898647,0.000332633,0.0004644954,0.00008867214],"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":[0.001075475,0.0002718338,0.01042793,0.0004573739,0.0002766573,0.0009808725,0.0004724513,0.1075445,0.1228581,0.07801182,0.005723216,0.6718998],"study_design_scores_gemma":[0.00001038368,0.00006505318,0.004596041,0.00001082306,0.00003098035,0.0005091425,0.00003813099,0.9595846,0.01919574,0.01278685,0.003137819,0.00003448069],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03604901,0.0001847273,0.9604601,0.0001109386,0.00003720474,0.00004249052,0.0001738968,0.00129355,0.001648092],"genre_scores_gemma":[0.3584075,0.0004718098,0.6361724,0.00009105477,0.00009210331,0.0001462011,0.0007034567,0.0003132434,0.003602201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003410426,"threshold_uncertainty_score":0.01140904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006565175324876837,"score_gpt":0.2480766108042301,"score_spread":0.2415114354793533,"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."}}