{"id":"W4240610667","doi":"10.1515/iupac.76.0348","title":"Proteomics","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Computer science; Relation (database); Hazard; Toxicology; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":[0.001582206,0.002650173,0.001861126,0.005250516,0.001289637,0.004401579,0.003023822,0.002164892,0.1003619],"category_scores_gemma":[0.007352046,0.0009551775,0.001887854,0.008354084,0.0004571159,0.002960335,0.002896236,0.002173942,0.1795922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001809487,"about_ca_system_score_gemma":0.003105964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009345815,"about_ca_topic_score_gemma":0.01685427,"domain_scores_codex":[0.9978812,0.0002897116,0.0003371541,0.0007873577,0.0005015721,0.0002029699],"domain_scores_gemma":[0.9975395,0.0006317625,0.0003240645,0.0007360324,0.0005734516,0.0001951895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001260347,0.00002519257,0.0009041285,0.00251778,0.00006501585,0.00005231574,0.00004209051,0.0002927311,0.0007524721,0.001283742,0.9823949,0.01154362],"study_design_scores_gemma":[0.00007109917,0.0000137744,0.002166764,0.0004213208,0.00003632807,0.0001132423,0.00003862153,0.0002348011,0.0005165378,0.001970119,0.9943908,0.00002651846],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001587738,0.0005750828,0.0004180383,0.000111977,0.00005567194,0.00002518506,0.9951696,0.001134979,0.002350704],"genre_scores_gemma":[0.0002753523,0.000293595,0.000801341,0.0001216971,0.000009048907,0.00007531705,0.9973418,0.0001528339,0.0009289749],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1003619,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01232971454799001,"score_gpt":0.3963687816863108,"score_spread":0.3840390671383208,"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."}}