{"id":"W4229931868","doi":"10.1515/iupac.88.0876","title":"Gubernaculum","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Effects and risks of endocrine disrupting chemicals","field":"Environmental Science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005410342,0.001274165,0.002213312,0.009359091,0.001597073,0.007633698,0.003885537,0.002552553,0.4182099],"category_scores_gemma":[0.05947084,0.0008435127,0.001601516,0.01579659,0.0009886161,0.006228029,0.005466679,0.002945167,0.4056261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002132386,"about_ca_system_score_gemma":0.005044409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00948389,"about_ca_topic_score_gemma":0.01250025,"domain_scores_codex":[0.9946814,0.001385772,0.001002694,0.001282256,0.00121331,0.0004345803],"domain_scores_gemma":[0.9750714,0.008705612,0.002193248,0.005292326,0.007203176,0.001534318],"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":[0.00002105804,0.000003771569,0.0001220388,0.0005398418,0.000009224983,0.000007179705,0.0000209705,0.00002767254,0.00002291279,0.0008026838,0.9941677,0.004254953],"study_design_scores_gemma":[0.00002451393,0.000003197181,0.0003469751,0.0006205976,0.000005991216,0.00001138642,0.00002832552,0.00003463393,0.00002363968,0.0009209202,0.9979717,0.000008066108],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000119434,0.0005911275,0.000628044,0.002255204,0.001539811,0.0001462015,0.9802073,0.001281866,0.01323108],"genre_scores_gemma":[0.0006911737,0.0008310359,0.001784646,0.002819185,0.0004673273,0.0008817874,0.9794328,0.001325595,0.01176645],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4182099,"threshold_uncertainty_score":0.8298529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130219253274788,"score_gpt":0.4575555617071058,"score_spread":0.4462533691743579,"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."}}