{"id":"W2186363219","doi":"10.1515/iupac.80.0211","title":"Lymphocyte Subpopulations in Human Exposure to Metals","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Contact Dermatitis and Allergies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Lymphocyte; Immunology; Mercury (programming language); Cadmium; Chemistry; Biology; Computer science","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":[0.001286058,0.0009896156,0.001601551,0.002658412,0.0004588344,0.001593711,0.001673602,0.001246795,0.01720353],"category_scores_gemma":[0.009464036,0.0003662708,0.001516489,0.004971165,0.0002195613,0.000768457,0.0014798,0.001199253,0.007254621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000913163,"about_ca_system_score_gemma":0.001086116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00816006,"about_ca_topic_score_gemma":0.009180738,"domain_scores_codex":[0.9983303,0.0002347742,0.0004724972,0.0005006649,0.0002795161,0.000182248],"domain_scores_gemma":[0.9969665,0.001016109,0.0007679734,0.0004621474,0.0006564296,0.0001307736],"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":[0.003202129,0.0002215319,0.2113483,0.020954,0.001771464,0.0002779718,0.0002353688,0.001931946,0.0009943247,0.001146475,0.7091303,0.04878617],"study_design_scores_gemma":[0.001123981,0.0002681716,0.4020503,0.005585777,0.001473972,0.001284452,0.00036603,0.001677086,0.001536303,0.003525591,0.580942,0.00016638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005964169,0.002432121,0.0002370718,0.0002270974,0.00005931748,0.00006877084,0.9897753,0.0001290809,0.001107198],"genre_scores_gemma":[0.01955166,0.001933045,0.0009368349,0.0003165299,0.00005575737,0.0008394907,0.9751219,0.00004937531,0.001195391],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01720353,"threshold_uncertainty_score":0.05755156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102916309220064,"score_gpt":0.4086356528082005,"score_spread":0.3876064897159998,"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."}}