{"id":"W4252306696","doi":"10.1515/iupac.88.1009","title":"Macrostomia","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Urologic and reproductive health conditions","field":"Medicine","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":[0.0009020121,0.0009009723,0.001339234,0.002929575,0.0005107138,0.001581072,0.001214927,0.001146077,0.08108398],"category_scores_gemma":[0.01019755,0.0003864648,0.001696015,0.004153409,0.0002853356,0.001249999,0.001271449,0.001372309,0.03130151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009787575,"about_ca_system_score_gemma":0.002083481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009089571,"about_ca_topic_score_gemma":0.01831269,"domain_scores_codex":[0.9987207,0.000174789,0.0004720945,0.0003313716,0.0001995359,0.0001015503],"domain_scores_gemma":[0.9956836,0.00128019,0.001204291,0.0007759812,0.0008446573,0.0002114193],"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":[0.0005598625,0.00003597821,0.01011976,0.00885397,0.0002131796,0.0001447308,0.00005371843,0.0002575077,0.0002948744,0.001388262,0.9532648,0.02481321],"study_design_scores_gemma":[0.000634921,0.00006433957,0.03821157,0.006589606,0.0002976499,0.0007364854,0.0001310551,0.0003175557,0.0004506325,0.002704676,0.9497916,0.00006983248],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005559456,0.0007413176,0.0001899824,0.0001379452,0.00005879747,0.00008962346,0.9954191,0.0001529594,0.002654366],"genre_scores_gemma":[0.002607986,0.001242767,0.0009670432,0.000486064,0.00005085017,0.0005681114,0.9915957,0.00008408615,0.002397375],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08108398,"threshold_uncertainty_score":0.2712529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02948788112560797,"score_gpt":0.4921183225277204,"score_spread":0.4626304414021125,"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."}}