{"id":"W4235860746","doi":"10.1515/iupac.88.1000","title":"Lymphatic","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Lymphatic System and Diseases","field":"Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000373676,0.0004879116,0.001183398,0.0002193163,0.0002026856,0.0001225426,0.0004145251,0.0003824589,0.003786501],"category_scores_gemma":[0.00204888,0.0003781132,0.0003602879,0.00008088032,0.0001902247,0.00008137634,0.0001434382,0.00006271982,0.00003002015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005698146,"about_ca_system_score_gemma":0.003617625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000235674,"about_ca_topic_score_gemma":0.0001511948,"domain_scores_codex":[0.9969062,0.00005960154,0.0005482049,0.0005275626,0.001532563,0.0004258389],"domain_scores_gemma":[0.9963712,0.00008674486,0.0004461592,0.002081136,0.0005226744,0.0004921209],"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":[0.0002811596,0.0005010276,0.0001963655,0.00159188,0.0002727358,0.0007922504,0.000007898328,1.403573e-7,0.000002242788,0.000001899048,0.9949118,0.001440529],"study_design_scores_gemma":[0.003309007,0.0002900238,0.0007209912,0.002155613,0.001312179,0.000259359,0.00003569166,0.000005294772,0.000006746865,0.00004114404,0.9914993,0.0003646176],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002601731,0.004070893,0.00001012762,0.0004584752,0.001115716,0.0006142734,0.9928076,0.00008988551,0.0005728262],"genre_scores_gemma":[0.0003192404,0.0005234749,0.00003048343,0.0007253587,0.00192128,0.00003018803,0.9941566,0.00005028933,0.002243066],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.003756481,"threshold_uncertainty_score":0.9998671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02328125631194787,"score_gpt":0.4557344799593995,"score_spread":0.4324532236474516,"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."}}