{"id":"W2526195589","doi":"10.1002/pmic.201600210","title":"PeptideTracker: A knowledge base for collecting and storing information on protein concentrations in biological tissues","year":2016,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; Island Health; University of Victoria","funders":"Genome British Columbia; Bundesministerium für Bildung und Forschung; Genome Canada","keywords":"Knowledge base; Base (topology); Computer science; Content (measure theory); Information retrieval; Computational biology; Data science; World Wide Web; Biology; Mathematics","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":[0.003273783,0.002951226,0.002750701,0.011093,0.001382152,0.005560731,0.005845337,0.003290101,0.01791687],"category_scores_gemma":[0.01201738,0.001846116,0.001711113,0.008197112,0.000894836,0.007504282,0.003525619,0.002350977,0.02152257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114081,"about_ca_system_score_gemma":0.004701455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008253358,"about_ca_topic_score_gemma":0.01004869,"domain_scores_codex":[0.9986113,0.0001241126,0.0003258974,0.0003995388,0.0004557024,0.00008339858],"domain_scores_gemma":[0.9943633,0.00242632,0.0006959217,0.001214421,0.001024026,0.0002759421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001708839,0.0005693163,0.007678018,0.008642583,0.0008598997,0.001845575,0.0006516371,0.01755752,0.04621202,0.01611619,0.2372135,0.6609449],"study_design_scores_gemma":[0.0005486095,0.0003970687,0.007988686,0.002275949,0.001446912,0.00283633,0.0005722077,0.1059539,0.1087927,0.08642765,0.6819458,0.0008141889],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0066427,0.003938576,0.6068806,0.0006540021,0.0003285192,0.0005986138,0.2061927,0.165662,0.009102353],"genre_scores_gemma":[0.03902855,0.009348653,0.6072105,0.0007697105,0.0002178689,0.001350831,0.3288174,0.006651231,0.006605264],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01791687,"threshold_uncertainty_score":0.05993783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03500092394128514,"score_gpt":0.3049154659485645,"score_spread":0.2699145420072794,"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."}}