{"id":"W4245155346","doi":"10.1515/iupac.88.0905","title":"Hydranencephaly","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Radioactive element chemistry and processing","field":"Chemistry","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; Philosophy; Data mining","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":[0.001144764,0.001308057,0.001602294,0.003524952,0.0006486523,0.00197364,0.001856896,0.001318644,0.1140063],"category_scores_gemma":[0.009939685,0.0004559322,0.001269019,0.004958101,0.0004239444,0.001625245,0.001630021,0.001281274,0.05401902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009902588,"about_ca_system_score_gemma":0.002231524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009218337,"about_ca_topic_score_gemma":0.01958329,"domain_scores_codex":[0.9989676,0.0001573144,0.0003154567,0.0003036441,0.0001586744,0.00009726285],"domain_scores_gemma":[0.9961724,0.001550105,0.0007516259,0.0007688713,0.0005575478,0.0001995045],"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":[0.000312839,0.00001890269,0.003635542,0.003867508,0.0001273346,0.000212421,0.00003235386,0.0002024285,0.0001632842,0.0009654778,0.9741896,0.01627234],"study_design_scores_gemma":[0.0004113297,0.0000355989,0.01850296,0.004071505,0.0001685763,0.00114314,0.0001126277,0.0001951255,0.0003527506,0.003183759,0.9717624,0.00006023781],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003179967,0.0004369947,0.0001308764,0.0001201585,0.00004219162,0.0000392116,0.996799,0.0001691047,0.001944441],"genre_scores_gemma":[0.001354737,0.0007654542,0.0005515966,0.0002374834,0.00002683903,0.0002717031,0.9952967,0.00007880144,0.001416696],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1140063,"threshold_uncertainty_score":0.3813892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789329094829458,"score_gpt":0.4212563909426565,"score_spread":0.403363099994362,"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."}}