{"id":"W4402633662","doi":"10.54434/candj.182","title":"Mercury Screening for At-Risk Populations","year":2024,"lang":"en","type":"article","venue":"CAND Journal","topic":"Blood donation and transfusion practices","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Confederation College","funders":"","keywords":"Mercury (programming language); Environmental health; Environmental science; Environmental chemistry; Medicine; Computer science; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008969735,0.0001737468,0.0002619172,0.001380921,0.001742271,0.0008369899,0.0005523775,0.0005699763,0.003791182],"category_scores_gemma":[0.003439411,0.00009038497,0.0002029871,0.0009322846,0.0004251022,0.0004944788,0.000954794,0.0008358426,0.0004563764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001462903,"about_ca_system_score_gemma":0.006140545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1189062,"about_ca_topic_score_gemma":0.1650751,"domain_scores_codex":[0.9993209,0.0001443084,0.00004689497,0.00004021642,0.000261159,0.0001865542],"domain_scores_gemma":[0.9988497,0.0001657727,0.0001924301,0.0000298274,0.0003719347,0.0003902927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001088746,0.0002652579,0.6991265,0.0004557886,0.00002614727,0.004988409,0.006320239,0.0001790742,0.001964686,0.002027655,0.0278214,0.2567158],"study_design_scores_gemma":[0.00002424165,0.0004625109,0.8424855,0.002793874,0.00008798154,0.01160163,0.01892777,0.0008766331,0.002694335,0.005820413,0.1141525,0.00007254114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8332788,0.0239706,0.004841529,0.04180826,0.0007221487,0.0005079393,0.0008724195,0.0002425378,0.0937558],"genre_scores_gemma":[0.9733067,0.01259062,0.004188377,0.003235193,0.0002418812,0.00008623437,0.0002295825,0.00001753045,0.006103874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1189062,"threshold_uncertainty_score":0.2364281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05554509169400797,"score_gpt":0.2984162012383205,"score_spread":0.2428711095443125,"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."}}