{"id":"W2208909590","doi":"10.1021/acs.estlett.5b00319","title":"A High-Precision Passive Air Sampler for Gaseous Mercury","year":2015,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; Environment and Climate Change Canada; University of Toronto","funders":"Environment Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Mercury (programming language); Environmental science; Replicate; Sampling (signal processing); Environmental chemistry; Elemental mercury; Indoor air; Sorbent; Atmospheric sciences; Meteorology; Chemistry; Environmental engineering; Geology","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":[0.0007168295,0.0005272228,0.0004647166,0.0004854833,0.0003039377,0.0003808396,0.0007058205,0.0006658004,0.001898775],"category_scores_gemma":[0.0007440679,0.0003428633,0.0003271221,0.0003562405,0.0002795412,0.0004289868,0.0004989813,0.0005174616,0.001058832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00027944,"about_ca_system_score_gemma":0.0005241335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001116789,"about_ca_topic_score_gemma":0.00362452,"domain_scores_codex":[0.9990089,0.000134836,0.00004156814,0.0002768709,0.0005005143,0.0000372097],"domain_scores_gemma":[0.9996417,0.00009968216,0.00005598915,0.00007327524,0.0001045111,0.00002480467],"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":[0.00007113278,0.0000410069,0.003498131,0.0001014935,0.00002253764,0.00003486752,0.00006121273,0.0004540737,0.9705479,0.0002253146,0.0004896222,0.02445267],"study_design_scores_gemma":[0.00003009226,0.0005637843,0.01626341,0.00001239271,0.00006548217,0.0005177697,0.0000392937,0.0107563,0.957325,0.0001973763,0.01418739,0.0000416965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3782547,0.0009816895,0.6060989,0.0002050414,0.000128485,0.0007239453,0.003223403,0.00473301,0.005650721],"genre_scores_gemma":[0.4822641,0.0007962257,0.4982629,0.0002478418,0.00009728994,0.001190536,0.003335366,0.0002738996,0.01353193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001898775,"threshold_uncertainty_score":0.006352007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380323357251306,"score_gpt":0.2451725939084057,"score_spread":0.2313693603358927,"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."}}