{"id":"W2768363351","doi":"10.1038/s41598-017-16713-7","title":"Proof of concept for a passive sampler for monitoring of gaseous elemental mercury in artisanal gold mining","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Laboratório Nacional de Nanotecnologia; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ciência sem Fronteiras; Fundação de Amparo à Pesquisa do Estado de São Paulo; University of Victoria; U.S. Department of State","keywords":"Mercury (programming language); Elemental mercury; Environmental chemistry; Proof of concept; Environmental science; Gold mining; Computer science; Data science; Chemistry; Operating system; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008567407,0.000600995,0.0003709702,0.0003115988,0.0002699943,0.000442973,0.001091026,0.001266995,0.001876935],"category_scores_gemma":[0.0005788165,0.0003470051,0.0004427181,0.0001530764,0.0003866393,0.0005288415,0.0004634188,0.0009142138,0.000891207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003131539,"about_ca_system_score_gemma":0.0005054565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006108857,"about_ca_topic_score_gemma":0.0009133266,"domain_scores_codex":[0.9994795,0.00005750046,0.00002161451,0.0001109059,0.0002876786,0.00004295977],"domain_scores_gemma":[0.9997043,0.00006632668,0.00005984976,0.00003168047,0.0001032864,0.00003448959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005625525,0.00008433461,0.0003024641,0.0001472356,0.00001275687,0.0001811502,0.00003819901,0.0001744879,0.9940554,0.0003603883,0.000394875,0.004192405],"study_design_scores_gemma":[0.00003289657,0.0006669573,0.0009277962,0.00001747444,0.00001832095,0.0005396067,0.00004216558,0.002570779,0.9865299,0.0001054434,0.008529305,0.00001926775],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7053289,0.005278478,0.2693974,0.002407896,0.001409153,0.002711557,0.002739841,0.002711811,0.008014985],"genre_scores_gemma":[0.7871788,0.002042714,0.1991518,0.0006709431,0.0001061345,0.001234976,0.0009122145,0.0000594023,0.008643051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001876935,"threshold_uncertainty_score":0.006278932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04491634378594925,"score_gpt":0.3194729773686387,"score_spread":0.2745566335826894,"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."}}