{"id":"W1528196427","doi":"10.1111/j.1471-4159.2010.06760.x","title":"Analysis of peptides in prohormone convertase 1/3 null mouse brain using quantitative peptidomics","year":2010,"lang":"en","type":"article","venue":"Journal of Neurochemistry","topic":"Neuropeptides and Animal Physiology","field":"Neuroscience","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Chemistry; National Institute of Diabetes and Digestive and Kidney Diseases; Howard Hughes Medical Institute; National Institute on Drug Abuse; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Prohormone convertase; Peptide; Prohormone; Proenkephalin; Carboxypeptidase; Neuropeptide; Proteolysis; Biochemistry; Chemistry; Cholecystokinin; Biology; Proglucagon; Opioid peptide; Endocrinology; Enzyme; Hormone; Glucagon-like peptide-1","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001953282,0.0001716633,0.0005407444,0.0002939511,0.00004188151,0.00002493437,0.0004150254,0.00007865804,0.00003972676],"category_scores_gemma":[0.001804966,0.0001523032,0.0002823662,0.0006128281,0.0002362143,0.0001955672,0.00008270938,0.0006988253,0.000002022904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001796696,"about_ca_system_score_gemma":0.0000944745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000133835,"about_ca_topic_score_gemma":0.000008877828,"domain_scores_codex":[0.9984244,0.00008938904,0.000718599,0.0002711431,0.00026192,0.0002345957],"domain_scores_gemma":[0.9981409,0.0005350051,0.0008259985,0.0002290633,0.0001503536,0.0001186643],"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":[0.0002950822,0.0002108323,0.001587526,0.0000285012,0.00003735195,0.0001786969,0.0001406078,0.0008445195,0.9964806,0.00007691242,0.00007824817,0.00004105624],"study_design_scores_gemma":[0.0005715252,0.0002354354,0.005922052,0.00002038723,0.0001050078,0.0001448735,0.0001149146,0.004565018,0.9879746,0.00007933684,0.0001262881,0.0001405851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995494,0.00001124325,0.00001638907,0.00009465913,0.0000872826,0.00005713016,0.00002642257,0.000006297914,0.0001511169],"genre_scores_gemma":[0.9989021,0.00003154536,0.0004956704,0.0004262759,0.00005343206,6.229435e-7,0.000001118719,0.00001974532,0.00006943771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008506089,"threshold_uncertainty_score":0.6210744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03927102908298534,"score_gpt":0.3160401387651406,"score_spread":0.2767691096821553,"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."}}