{"id":"W4232860973","doi":"10.1515/iupac.78.0573","title":"Specimens","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Glossary; Relation (database); Chemical nomenclature; Computer science; Field (mathematics); Management science; Data science; Engineering; Chemistry; Data mining; Mathematics; Linguistics","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":[0.002066632,0.001628905,0.001310918,0.006876555,0.001371945,0.003102293,0.002871094,0.002135032,0.2490777],"category_scores_gemma":[0.01534365,0.00077509,0.001165364,0.01026919,0.0006474575,0.00349175,0.00253745,0.002264089,0.2044544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002351354,"about_ca_system_score_gemma":0.004204927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02284262,"about_ca_topic_score_gemma":0.03662889,"domain_scores_codex":[0.9973838,0.0004095864,0.0006400438,0.0007731882,0.0005390493,0.000254353],"domain_scores_gemma":[0.9932315,0.002074039,0.0007719909,0.001467542,0.002097817,0.0003569995],"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":[0.00003715775,0.000008831817,0.0005750559,0.0006957129,0.00001218931,0.00002190583,0.00003418819,0.00007898552,0.00008903833,0.0008412104,0.9942755,0.003330129],"study_design_scores_gemma":[0.00005326255,0.000005293969,0.00161275,0.000517424,0.00001136313,0.00004805093,0.0001148886,0.00006369995,0.00009277725,0.001390216,0.9960736,0.00001657245],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006800237,0.00007189903,0.0001040445,0.00008919114,0.00003294623,0.00002553734,0.998307,0.0001324487,0.001168936],"genre_scores_gemma":[0.0002534159,0.0001023292,0.0004453934,0.0001327342,0.00001374956,0.0002158867,0.9972059,0.00008971193,0.001540807],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2490777,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804157660443239,"score_gpt":0.423832619089801,"score_spread":0.4057910424853686,"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."}}