{"id":"W4235071255","doi":"10.1515/iupac.80.0219","title":"Interpretation","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Heavy Metal Exposure and Toxicity","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Lymphocyte; Mercury (programming language); Cadmium; Chemistry; Immunology; Biology; Metallurgy; Materials science; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003365005,0.001847758,0.001481135,0.003687625,0.0008073671,0.003167116,0.003106587,0.001814645,0.1562462],"category_scores_gemma":[0.02999362,0.0004407083,0.002832049,0.005648715,0.0005166371,0.001866322,0.001991551,0.001837263,0.07912385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00159455,"about_ca_system_score_gemma":0.003704355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008415747,"about_ca_topic_score_gemma":0.01239393,"domain_scores_codex":[0.9952152,0.000945699,0.0009942142,0.001662557,0.0007485948,0.0004337475],"domain_scores_gemma":[0.9908228,0.003345565,0.001104932,0.001828725,0.002611744,0.0002862123],"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":[0.001007512,0.0000702601,0.01248115,0.00640266,0.0004090151,0.0001226375,0.0001011152,0.0008145916,0.00022935,0.001808146,0.9418507,0.03470283],"study_design_scores_gemma":[0.0006732696,0.00008207619,0.0166064,0.003518112,0.0003708314,0.0002930366,0.0003693856,0.0008298678,0.0005847554,0.008317679,0.9682752,0.00007932831],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007771728,0.0004695925,0.0007353971,0.0004327685,0.0002490894,0.0002761084,0.9928308,0.0004116909,0.003817341],"genre_scores_gemma":[0.005524325,0.0004786861,0.002884455,0.0007031133,0.0001504939,0.001918234,0.9833649,0.0002613341,0.004714529],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8437538,"threshold_uncertainty_score":0.5226955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009473970704251,"score_gpt":0.3672746550257278,"score_spread":0.3571799153186853,"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."}}