{"id":"W4245701917","doi":"10.1515/iupac.88.1220","title":"Postpartum","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","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":[0.001431453,0.001119804,0.001111711,0.002892821,0.0009585676,0.003465476,0.002139812,0.001403576,0.3106687],"category_scores_gemma":[0.01409821,0.0005444865,0.001416851,0.006645204,0.0003152451,0.002918255,0.0022285,0.001638486,0.2691645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686963,"about_ca_system_score_gemma":0.002892805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02215711,"about_ca_topic_score_gemma":0.02761452,"domain_scores_codex":[0.9977702,0.0003738438,0.0004696676,0.0006373365,0.0004939709,0.0002549261],"domain_scores_gemma":[0.9944252,0.001375323,0.0006332845,0.001160948,0.002088923,0.0003164432],"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":[0.00009692882,0.0000122424,0.00115258,0.0007801578,0.00001964998,0.00001947941,0.00003366571,0.00004802624,0.00004300555,0.0009977985,0.9854402,0.01135631],"study_design_scores_gemma":[0.00007701159,0.00001076546,0.003606386,0.0006633356,0.00001624663,0.00005026518,0.0001089475,0.00006169647,0.00008901286,0.001078693,0.9942215,0.00001620887],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002036785,0.000204898,0.0001566977,0.0002892452,0.0001008424,0.00005231845,0.9904639,0.0003200773,0.008208422],"genre_scores_gemma":[0.001044747,0.0003678164,0.000491004,0.0005722515,0.00004998528,0.0002625298,0.9873943,0.0001808567,0.009636583],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6893313,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295215549241502,"score_gpt":0.4535700400032933,"score_spread":0.4306178845108782,"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."}}