{"id":"W1942779918","doi":"10.1016/j.dib.2015.10.003","title":"TAILS N-terminomic and proteomic datasets of healthy human dental pulp","year":2015,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Proteomics; Proteome; Pulp (tooth); Mass spectrometry; Tandem mass spectrometry; Metadata; Chromatography; Identifier; Computer science; Chemistry; Computational biology; Bioinformatics; Dentistry; Biology; Biochemistry; Medicine","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":[0.0005658481,0.0004708895,0.0005546155,0.001737174,0.0006135614,0.0005664253,0.0004116389,0.0005829739,0.003253319],"category_scores_gemma":[0.0008512792,0.0002058201,0.0004880767,0.00164226,0.0002658447,0.0003715731,0.001122524,0.0004199225,0.002762507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002407537,"about_ca_system_score_gemma":0.0006760159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000881804,"about_ca_topic_score_gemma":0.001926491,"domain_scores_codex":[0.9996006,0.0000307619,0.00005596202,0.0001192504,0.0001529653,0.00004043452],"domain_scores_gemma":[0.999631,0.00006384234,0.00006081616,0.00008039985,0.0001104908,0.00005349363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004317981,0.0003177823,0.03346313,0.002240269,0.0002130527,0.001600931,0.000430247,0.00118692,0.8571753,0.0008634762,0.01710567,0.08108523],"study_design_scores_gemma":[0.0002075369,0.001166212,0.520126,0.0003168162,0.000472302,0.01116247,0.0007255101,0.005720899,0.2892397,0.003509506,0.1671658,0.0001873113],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6976767,0.005722443,0.01316671,0.0002601773,0.00007007038,0.0002657715,0.2772121,0.0006358679,0.004990123],"genre_scores_gemma":[0.2761193,0.004984198,0.04966551,0.0002584352,0.00009133139,0.0007583193,0.6640111,0.0002479233,0.003863878],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.003253319,"threshold_uncertainty_score":0.01088339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05703018991855938,"score_gpt":0.3560454056550617,"score_spread":0.2990152157365022,"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."}}