{"id":"W4252718118","doi":"10.1515/iupac.87.0601","title":"Serotoninergic","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; Relation (database); Chemical nomenclature; Computer science; Psychology; Neuroscience; Chemistry; Linguistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003772906,0.0005027982,0.0005883523,0.0001653274,0.0001301629,0.00006033509,0.0005948355,0.0002736046,0.006337445],"category_scores_gemma":[0.0005680009,0.0003558952,0.0001903506,0.0001257074,0.0001624216,0.00009092789,0.0002855,0.0005234869,0.00002801844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334639,"about_ca_system_score_gemma":0.0003296116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001632134,"about_ca_topic_score_gemma":0.0000641539,"domain_scores_codex":[0.9975215,0.0001171649,0.0004327598,0.0006207952,0.0008368358,0.0004709205],"domain_scores_gemma":[0.9982666,0.0001729071,0.0003192757,0.0007621574,0.0003148317,0.0001641846],"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":[0.0004357092,0.0001390728,0.00001505883,0.0001189123,0.00009563504,0.0006748508,0.000007950515,2.848225e-7,0.00005557883,0.00001355385,0.9965962,0.001847184],"study_design_scores_gemma":[0.00062094,0.001373825,0.00006094955,0.0007523735,0.00006204798,0.000149403,0.00001513393,0.000007194667,0.00001104274,0.0001831961,0.9962746,0.0004892852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001877674,0.0005079392,0.00004715341,0.0002336494,0.0006018446,0.0002239479,0.9958937,0.0002213624,0.0003927357],"genre_scores_gemma":[0.0002946792,0.0003309614,0.0001032663,0.0001899293,0.001899093,0.00002359699,0.9950733,0.00006338048,0.00202185],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.006309426,"threshold_uncertainty_score":0.9998893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09395343089655644,"score_gpt":0.5042751457728716,"score_spread":0.4103217148763151,"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."}}