{"id":"W2092285598","doi":"10.1016/j.envres.2014.08.029","title":"Mercury levels in pregnant women, children, and seafood from Mexico City","year":2014,"lang":"en","type":"article","venue":"Environmental Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Public Health Ontario; McGill University","funders":"National Institute of Environmental Health Sciences; National Center for Research Resources; U.S. Public Health Service; National Institutes of Health; U.S. Environmental Protection Agency","keywords":"Mercury (programming language); Cord blood; Tuna; Urine; Population; Pregnancy; Offspring; Medicine; MERCURY EXPOSURE; Toxicology; Environmental health; Animal science; Physiology; Biology; Biomonitoring; Fishery; Endocrinology; Internal medicine; Fish <Actinopterygii>; Ecology","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":[0.0002017332,0.0002381079,0.0002199888,0.0008173726,0.0009102955,0.0004769874,0.0002470302,0.0004916291,0.001121036],"category_scores_gemma":[0.0007320411,0.0003722028,0.0002307677,0.001021938,0.0002778579,0.0001823393,0.0005554436,0.0003431021,0.0001530376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089838,"about_ca_system_score_gemma":0.000722056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1681106,"about_ca_topic_score_gemma":0.1822449,"domain_scores_codex":[0.9998615,0.00003192565,0.00001182232,0.00002956726,0.00002709182,0.00003819518],"domain_scores_gemma":[0.9998419,0.00003083948,0.00005733193,0.000007107822,0.00003183315,0.00003089577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001015754,0.00003528558,0.9947723,0.00001883856,0.00003156858,0.0002569778,0.001675926,0.00002785466,0.0007159307,0.00002394933,0.0001901389,0.002149676],"study_design_scores_gemma":[0.00000475968,0.00007473007,0.9964063,0.000008513983,0.00003112964,0.0001751297,0.002623942,0.00002182373,0.0001939055,0.000006763069,0.0004501525,0.000002705752],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990159,0.0001017818,0.00001883132,0.00006531566,0.000002205522,0.000005617039,0.000281717,0.000001470451,0.0005070165],"genre_scores_gemma":[0.99809,0.0003804968,0.00009506861,0.00007653035,0.000004698127,0.00002240981,0.0003277114,0.000001907533,0.001001212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1681106,"threshold_uncertainty_score":0.3342642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332987127998084,"score_gpt":0.3100446532565703,"score_spread":0.2667147819765894,"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."}}