{"id":"W4232537207","doi":"10.1515/iupac.88.1394","title":"Tail Bud","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; Philosophy; Data mining","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":[0.001625889,0.001622739,0.001355418,0.004013288,0.001254326,0.004785093,0.00266803,0.002091957,0.2203895],"category_scores_gemma":[0.012662,0.0007908191,0.002098719,0.006642095,0.0004625566,0.003577228,0.003655506,0.002104226,0.3004734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00169277,"about_ca_system_score_gemma":0.003197446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01947185,"about_ca_topic_score_gemma":0.03711855,"domain_scores_codex":[0.9979175,0.0003846868,0.0003839344,0.0005991748,0.0004319334,0.0002828321],"domain_scores_gemma":[0.9942878,0.001486224,0.0005119621,0.001722184,0.001632542,0.0003594235],"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":[0.00008000808,0.00001061219,0.0008761025,0.0008822066,0.00002171126,0.0000151879,0.00002656327,0.000109841,0.00006183879,0.0009462655,0.991625,0.005344591],"study_design_scores_gemma":[0.00009289313,0.00001199259,0.001901913,0.0005594957,0.00001573833,0.00003947371,0.00006516505,0.0001393155,0.0001366965,0.001456211,0.9955608,0.00002026079],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009057455,0.00009248446,0.0001242876,0.0001299417,0.00005223598,0.00002378786,0.9962405,0.0006768896,0.002569197],"genre_scores_gemma":[0.0003166469,0.0001254907,0.000383841,0.0001766045,0.00001638275,0.000109412,0.9964415,0.0002312749,0.002198838],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2203895,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02429693583037663,"score_gpt":0.4569126006549989,"score_spread":0.4326156648246223,"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."}}