{"id":"W4281477912","doi":"10.3233/shti220385","title":"Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases","year":2022,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"Health Canada; Partenariat Canadien Contre Le Cancer; Fondation de la recherche en santé du Nouveau-Brunswick","keywords":"Arsenic; Arsenic contamination of groundwater; Prostate cancer; Cancer; Biomarker; Environmental health; Carcinogen; Environmental science; Environmental chemistry; Chemistry; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006840996,0.0003939053,0.0003072552,0.001384376,0.0001975516,0.0004995781,0.0002562597,0.0003423408,0.0006245741],"category_scores_gemma":[0.002526634,0.00009908543,0.000437275,0.0007454977,0.0001644684,0.0001911198,0.0002507386,0.0003112472,0.0001979598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004874025,"about_ca_system_score_gemma":0.0003815444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01974199,"about_ca_topic_score_gemma":0.02418278,"domain_scores_codex":[0.9997186,0.00008862363,0.00002082612,0.00007632311,0.00005427998,0.00004129336],"domain_scores_gemma":[0.9990655,0.0004716332,0.0001488634,0.00008401091,0.0001679343,0.00006208415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005494214,0.0002610352,0.9024457,0.00004233979,0.0002752399,0.000156434,0.00008373714,0.03222029,0.01177084,0.00009429224,0.000348447,0.0517523],"study_design_scores_gemma":[0.00001958653,0.0005569796,0.6221631,0.00001840012,0.0001021325,0.0003177029,0.0002576748,0.3635652,0.01152949,0.0007675133,0.0006775109,0.00002487504],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887866,0.00009637199,0.009367339,0.00005917628,0.000006565136,0.00004487239,0.0009632182,0.0001309433,0.0005449581],"genre_scores_gemma":[0.991951,0.00003910774,0.006869267,0.00002168409,0.000003035795,0.00001780528,0.000740791,0.000007059787,0.0003502062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01974199,"threshold_uncertainty_score":0.03925413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04681634820879477,"score_gpt":0.3295422734415374,"score_spread":0.2827259252327426,"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."}}