{"id":"W2334645823","doi":"10.5194/acpd-13-17021-2013","title":"Understanding atmospheric mercury speciation and mercury in snow over time at Alert, Canada","year":2013,"lang":"en","type":"article","venue":"","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Mercury (programming language); Aerosol; Snow; Elemental mercury; Environmental chemistry; Particulates; Environmental science; Chemistry; Atmospheric sciences; Meteorology; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002787888,0.0002834747,0.0003130602,0.001579663,0.001375031,0.0009555685,0.0005399217,0.0002602765,0.0008713947],"category_scores_gemma":[0.0005161532,0.0001595715,0.0003302182,0.003070447,0.0003392998,0.0004440799,0.000490598,0.0002506256,0.000117627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01154346,"about_ca_system_score_gemma":0.01177133,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9908966,"about_ca_topic_score_gemma":0.9962859,"domain_scores_codex":[0.9997645,0.00000855388,0.000009083787,0.0000404907,0.0001004664,0.00007696385],"domain_scores_gemma":[0.9994444,0.00002434702,0.00006562189,0.000008259881,0.0003834131,0.00007388609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006329863,0.00001596353,0.986384,0.00005290271,0.00008375914,0.0001424344,0.0008327055,0.0005689823,0.003170865,0.0001257173,0.0009027015,0.00765668],"study_design_scores_gemma":[0.000001069658,0.000005800897,0.9970756,0.00001052105,0.00001305687,0.00001710315,0.0008689831,0.0005840974,0.0002545684,0.00002510708,0.001139533,0.000004554157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916496,0.0007690485,0.0002207605,0.000169541,0.000005452387,0.0000138208,0.004572607,0.0000175874,0.002581622],"genre_scores_gemma":[0.9950507,0.000435369,0.0003937815,0.0000641447,0.000003224299,0.000005859971,0.00283558,0.000006439511,0.001204873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01154346,"threshold_uncertainty_score":0.08375412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02231143075753121,"score_gpt":0.2123569103594716,"score_spread":0.1900454796019404,"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."}}