{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001631028,0.0001112109,0.0001176369,0.00004970986,0.0001392519,0.0000320791,0.00008318285,0.0001088915,0.00001815068],"category_scores_gemma":[0.0003242284,0.00008546481,0.00002512775,0.00008720591,0.00004635098,0.0002044271,0.00003587577,0.0001064904,0.000006512149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000160283,"about_ca_system_score_gemma":0.00004263304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006600666,"about_ca_topic_score_gemma":0.00001073929,"domain_scores_codex":[0.9993143,0.000008726207,0.0002513524,0.0001779111,0.00004267418,0.0002050152],"domain_scores_gemma":[0.9995179,0.0001193562,0.000108162,0.0001404956,0.00006297793,0.00005110314],"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":[0.0001131309,0.00007502339,0.003254717,0.00009081438,0.000005196419,2.795337e-7,0.0003457192,0.000007863862,0.9433347,0.02861949,0.00005119916,0.02410189],"study_design_scores_gemma":[0.0007994189,0.00007518272,0.00007024244,0.0002180005,0.000002276162,0.000002135748,0.00009032427,0.000736248,0.9772295,0.01106682,0.009527581,0.0001822661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822407,0.0000341252,0.1141693,0.0003979859,0.00001138479,0.001587664,0.00008064515,0.0001365623,0.001341659],"genre_scores_gemma":[0.8575886,0.00004880383,0.138902,0.00001966598,0.00005973475,0.003112569,0.00001494748,0.00001118715,0.0002425056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03389483,"threshold_uncertainty_score":0.3485154,"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."}}