{"id":"W4244310743","doi":"10.1515/iupac.87.0343","title":"Lethargy","year":2016,"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; Chemical nomenclature; Lethargy; Relation (database); Computer science; Psychology; Chemistry; Linguistics; Psychiatry; Data mining; Philosophy; Organic chemistry","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":[0.0009354112,0.0008851122,0.001056567,0.00219991,0.0007977475,0.002641848,0.001570248,0.001480945,0.2673666],"category_scores_gemma":[0.01766591,0.0003319536,0.001149164,0.004481265,0.0003466873,0.00221605,0.001890473,0.001436747,0.1570124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518964,"about_ca_system_score_gemma":0.002226951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01772657,"about_ca_topic_score_gemma":0.02938958,"domain_scores_codex":[0.9985316,0.0002626535,0.0003485955,0.0003715429,0.0003147325,0.000170852],"domain_scores_gemma":[0.993511,0.002327694,0.001079782,0.000990817,0.00170306,0.0003876018],"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":[0.000102038,0.000009212375,0.001448502,0.000659749,0.00001710519,0.00002971685,0.00001667377,0.00005751226,0.00001819033,0.0004535054,0.9891995,0.007988222],"study_design_scores_gemma":[0.0001096802,0.00001533684,0.005426513,0.0009402191,0.00002539797,0.0001408546,0.0001258279,0.000109232,0.00007946481,0.001547163,0.9914584,0.00002198013],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000384122,0.0004125733,0.0001272917,0.0007098925,0.000156741,0.00005096113,0.9874309,0.000309526,0.01041806],"genre_scores_gemma":[0.003121129,0.0007860318,0.0005001354,0.001492637,0.0001273995,0.0002728665,0.9818295,0.0001683467,0.011702],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7326334,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795918410896359,"score_gpt":0.4280299513214706,"score_spread":0.4100707672125071,"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."}}