{"id":"W4232840593","doi":"10.1515/iupac.88.1255","title":"Protein Kinase","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Melanoma and MAPK Pathways","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Data mining; 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":[0.0007755273,0.001900323,0.001642268,0.003680998,0.000926102,0.002951832,0.001863654,0.001654375,0.08515434],"category_scores_gemma":[0.004536136,0.000658164,0.001829319,0.006680224,0.0003534671,0.001981888,0.001576066,0.00197722,0.1109784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001433617,"about_ca_system_score_gemma":0.002646999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0119538,"about_ca_topic_score_gemma":0.01964724,"domain_scores_codex":[0.9988368,0.0001385444,0.0002467224,0.0004056504,0.0002291354,0.0001432186],"domain_scores_gemma":[0.9985179,0.0003952613,0.0002794058,0.0003680125,0.0003257609,0.0001136255],"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":[0.000457712,0.00003406112,0.003198304,0.005771979,0.0001272315,0.00008906911,0.00005487922,0.0005882762,0.0008669164,0.001624624,0.9696323,0.01755457],"study_design_scores_gemma":[0.0001568213,0.00002473267,0.006684404,0.000911191,0.00008844415,0.0001826807,0.00005537914,0.0002556921,0.0006556525,0.001764659,0.9891806,0.00003973073],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002365121,0.0007658626,0.0001505549,0.00009746396,0.00004267691,0.00001944687,0.9963122,0.0002893703,0.002085864],"genre_scores_gemma":[0.0007797508,0.0007183854,0.0004302285,0.0001315744,0.00001092151,0.00009893444,0.9964036,0.00006554426,0.001361158],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08515434,"threshold_uncertainty_score":0.2848697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392176396267638,"score_gpt":0.3801489457770687,"score_spread":0.3662271818143923,"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."}}