{"id":"W4249838532","doi":"10.1515/iupac.88.1288","title":"Rete","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; Data mining; Philosophy","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":[0.002387522,0.001761947,0.001488956,0.005448021,0.001251915,0.00489243,0.002948196,0.002198114,0.2421862],"category_scores_gemma":[0.02104322,0.0009499558,0.002252993,0.008581595,0.0005883637,0.004118857,0.003824894,0.002321572,0.2955187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990931,"about_ca_system_score_gemma":0.004163798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02132911,"about_ca_topic_score_gemma":0.03963472,"domain_scores_codex":[0.9966499,0.000634302,0.0007075131,0.0009076491,0.0007202126,0.0003803564],"domain_scores_gemma":[0.9904726,0.002865465,0.000958871,0.002549045,0.002688776,0.0004653343],"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":[0.00005957141,0.000009175308,0.0006977046,0.0009346523,0.00002526727,0.00001418301,0.00002775177,0.00008332304,0.00005424318,0.0008625736,0.9933425,0.003889057],"study_design_scores_gemma":[0.00009377968,0.00001094617,0.001691,0.0005708962,0.00001692755,0.00003894813,0.00006369629,0.00008484133,0.0001185269,0.001122946,0.9961665,0.00002097454],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006546186,0.00008167732,0.00009198365,0.0001403573,0.00006155675,0.00002336524,0.9970403,0.0004133265,0.002081885],"genre_scores_gemma":[0.0002765322,0.0001173688,0.0003267186,0.0001904319,0.0000232308,0.0001282403,0.9964936,0.0002404672,0.002203417],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7578138,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635801740284833,"score_gpt":0.4704181305666245,"score_spread":0.4440601131637762,"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."}}