{"id":"W4236319174","doi":"10.1515/iupac.88.0725","title":"Embryo","year":2017,"lang":"it","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":[],"consensus_categories":[],"category_scores_codex":[0.001203656,0.001266336,0.001093685,0.002978185,0.000894712,0.002800641,0.00207211,0.001536998,0.140676],"category_scores_gemma":[0.008206359,0.000572778,0.00156869,0.004133564,0.0003772297,0.002148983,0.002328867,0.001623575,0.1543153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001398042,"about_ca_system_score_gemma":0.002385417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01417366,"about_ca_topic_score_gemma":0.026488,"domain_scores_codex":[0.9985241,0.0002537693,0.0002829659,0.0004279448,0.0003470835,0.00016408],"domain_scores_gemma":[0.9968618,0.0009013516,0.0003720141,0.0007547968,0.0009253725,0.0001845609],"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":[0.0001446352,0.00001691082,0.001754538,0.002042252,0.00004311099,0.00004183839,0.00004087069,0.0002023413,0.0002464153,0.001234945,0.983233,0.01099912],"study_design_scores_gemma":[0.0000874501,0.00001392066,0.003296173,0.0006906534,0.00002698236,0.00007881456,0.00005714442,0.0001374798,0.0002463548,0.00113639,0.9942101,0.00001862327],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001637449,0.0002163554,0.000166456,0.0001167694,0.00005845857,0.00003304019,0.9960693,0.0003918961,0.002783935],"genre_scores_gemma":[0.0005941498,0.0002513409,0.0005282717,0.0002402727,0.00001684613,0.0001306749,0.9960886,0.0001099948,0.002039832],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.140676,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137070746202152,"score_gpt":0.4544534403686475,"score_spread":0.4407463657484323,"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."}}