{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001548087,0.0004307128,0.0004626599,0.00007640622,0.0001348687,0.00004409702,0.0006815141,0.0005740961,0.006471538],"category_scores_gemma":[0.0001730958,0.0003483937,0.0001691939,0.00009585164,0.0001372158,0.00006117163,0.000248023,0.0006662455,0.000003747544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004812435,"about_ca_system_score_gemma":0.000446832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003358933,"about_ca_topic_score_gemma":0.00005769966,"domain_scores_codex":[0.9980372,0.000009297981,0.0004569554,0.0005729769,0.0005273288,0.0003961977],"domain_scores_gemma":[0.9977856,0.00004425439,0.0003631494,0.001361876,0.0002975709,0.0001475642],"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":[0.00004769078,0.0001101751,0.000001398882,0.0001744488,0.00003410927,0.00001006774,0.000001385058,4.700719e-7,0.002052502,0.00007303945,0.9950719,0.002422809],"study_design_scores_gemma":[0.0003556071,0.00002626365,1.43805e-7,0.0003421869,0.00004821546,0.00001330758,0.000003896346,0.000002717865,0.01107367,0.003326125,0.9843481,0.0004597826],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004129506,0.0001692765,0.008379117,0.0004434576,0.00006616946,0.0003433692,0.9899677,0.000254653,0.0003349323],"genre_scores_gemma":[0.000002503885,0.001377315,0.007088045,0.0001210493,0.0009254728,0.0003226912,0.9887182,0.00006781183,0.001376897],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01072382,"threshold_uncertainty_score":0.9998968,"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."}}