{"id":"W4250214438","doi":"10.1515/iupac.79.1061","title":"Conjunctiva","year":2016,"lang":"es","type":"dataset","venue":"IUPAC Standards Online","topic":"Ocular Oncology and Treatments","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006361814,0.0009560079,0.001762435,0.0003502658,0.0002637455,0.00004407787,0.000386582,0.001296409,0.0179324],"category_scores_gemma":[0.001137487,0.0006432936,0.0004722138,0.0002414527,0.000635269,0.0001177924,0.0002526456,0.0009476848,0.0001725431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306431,"about_ca_system_score_gemma":0.00268285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008469992,"about_ca_topic_score_gemma":0.0001575169,"domain_scores_codex":[0.9955538,0.0002733426,0.0008035072,0.001039221,0.0014079,0.0009222116],"domain_scores_gemma":[0.9963155,0.0003232606,0.0004770961,0.001539473,0.0007935905,0.0005510901],"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":[0.002224774,0.002202162,0.002516231,0.0003984298,0.002550821,0.002985896,0.00002044546,1.105764e-7,0.00003163797,0.00007941002,0.9696298,0.01736029],"study_design_scores_gemma":[0.01100137,0.003025782,0.001616731,0.002338375,0.002580806,0.0001923184,0.00004882635,0.000001755695,0.0001199249,0.0001790552,0.9782951,0.000599894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002664478,0.004448859,0.00009225214,0.00321263,0.002883123,0.0009359469,0.9849035,0.0001052507,0.0007540182],"genre_scores_gemma":[0.001858897,0.005524452,0.00006315314,0.001195271,0.003054054,0.00003538746,0.9832528,0.0001070578,0.004908935],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01775985,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0155425432132578,"score_gpt":0.4189251817286552,"score_spread":0.4033826385153974,"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."}}