{"id":"W4232571381","doi":"10.1515/iupac.87.0294","title":"Hen Test","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Animal testing and alternatives","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Chemical nomenclature; Relation (database); Test (biology); Computer science; Psychology; Chemistry; Linguistics; Biology; Philosophy; Data mining; 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":[],"consensus_categories":[],"category_scores_codex":[0.001317133,0.001547769,0.001110422,0.00258433,0.0008890704,0.002916419,0.002741066,0.001796958,0.1597614],"category_scores_gemma":[0.01054111,0.0005444018,0.001569568,0.002856229,0.000422058,0.002187279,0.002095651,0.001653749,0.1605837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148736,"about_ca_system_score_gemma":0.002844138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01540457,"about_ca_topic_score_gemma":0.03350323,"domain_scores_codex":[0.998406,0.0002500213,0.0002305236,0.0006007844,0.0003074591,0.0002052223],"domain_scores_gemma":[0.9962839,0.001121371,0.0003325627,0.001033243,0.0009702473,0.0002587749],"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":[0.0002171651,0.00002905013,0.003313539,0.0008747526,0.000056532,0.00004074962,0.00002655331,0.000366945,0.00009728315,0.00127734,0.9840139,0.009686157],"study_design_scores_gemma":[0.0002463112,0.00003398976,0.005012766,0.0004628011,0.000043227,0.0001116566,0.0001027552,0.0007321215,0.0003938586,0.003099028,0.9897318,0.00002964238],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003275597,0.000135262,0.0002474581,0.0001832169,0.00006668342,0.00004271619,0.9943594,0.001026111,0.003611513],"genre_scores_gemma":[0.001312704,0.0001277195,0.0008332185,0.0002351652,0.00002390642,0.0001328174,0.9940209,0.0002167938,0.003096655],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1597614,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09442676555541211,"score_gpt":0.5028439099595521,"score_spread":0.40841714440414,"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."}}