{"id":"W4242005492","doi":"10.1515/iupac.79.1875","title":"Pyrexia","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Hematological disorders and diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Computer science; Toxicology; Chemistry; Philosophy; Biology; Linguistics; Organic chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002199402,0.0003885729,0.0009322439,0.0001410822,0.00006212775,0.00002438918,0.0001962613,0.0005903928,0.01084544],"category_scores_gemma":[0.003523743,0.0002375852,0.0002403415,0.0001451124,0.0001826044,0.00002832412,0.0001235539,0.0004869871,0.00002181556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674195,"about_ca_system_score_gemma":0.0007718655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003793931,"about_ca_topic_score_gemma":0.0001016444,"domain_scores_codex":[0.9976911,0.00003577851,0.0004556548,0.0004478463,0.0009250061,0.0004446115],"domain_scores_gemma":[0.9980739,0.0002303882,0.0001478622,0.0007656963,0.0004246841,0.000357482],"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":[0.0002744108,0.0005257055,0.00006196803,0.0003056129,0.00008708744,0.0005778264,0.000002036369,5.277662e-8,0.000002152733,0.0000431112,0.9958454,0.002274573],"study_design_scores_gemma":[0.001709892,0.0006783577,0.0001314525,0.0008850594,0.0002655852,0.00007567431,0.00001122273,0.00000124856,0.000008411305,0.0004704425,0.9954572,0.0003054503],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001129932,0.001502339,0.0001294822,0.002946186,0.0004188831,0.0003810397,0.9939932,0.00009034802,0.0004255959],"genre_scores_gemma":[0.0000323908,0.003084337,0.00007739124,0.002309592,0.0006481857,0.0000147981,0.9931916,0.00003393735,0.0006077716],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01082363,"threshold_uncertainty_score":0.9900588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040806312874699,"score_gpt":0.4315368458564847,"score_spread":0.4111287827277377,"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."}}