{"id":"W4250460423","doi":"10.1515/iupac.88.0690","title":"Disc, Embryonic","year":2017,"lang":"it","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","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":[0.001427023,0.001493987,0.001466933,0.00381253,0.001611578,0.006181347,0.002649542,0.001899868,0.280878],"category_scores_gemma":[0.01546625,0.0007642162,0.001495804,0.005872705,0.0006573949,0.002940177,0.003019532,0.002119851,0.3368616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588875,"about_ca_system_score_gemma":0.003810133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127921,"about_ca_topic_score_gemma":0.02090829,"domain_scores_codex":[0.9981323,0.0002963924,0.0002864119,0.0006039725,0.0004277202,0.0002532542],"domain_scores_gemma":[0.9952964,0.00138767,0.0004349867,0.001330033,0.001219257,0.0003316622],"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":[0.00007088735,0.000006563444,0.0005556186,0.0008285817,0.00001606751,0.00001444323,0.00002935307,0.00007908795,0.00006156076,0.001048976,0.9927027,0.004586165],"study_design_scores_gemma":[0.00007155518,0.000007771764,0.001118907,0.0003692125,0.00001411931,0.00003702175,0.00004637328,0.00009300932,0.0001218857,0.001512599,0.996595,0.00001265773],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009116722,0.0001481493,0.0001330917,0.0002055829,0.0001172133,0.00002188092,0.9940225,0.0009869247,0.004273445],"genre_scores_gemma":[0.0006020929,0.0002215159,0.0005479507,0.0003256842,0.0000384476,0.0001107537,0.9941046,0.0004817549,0.003567219],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.280878,"threshold_uncertainty_score":0.9396304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816753839392105,"score_gpt":0.4215258052681272,"score_spread":0.4033582668742062,"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."}}