{"id":"W2751001186","doi":"10.1021/acs.est.7b03683","title":"A Pulse of Mercury and Major Ions in Snowmelt Runoff from a Small Arctic Alaska Watershed","year":2017,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"U.S. Army Corps of Engineers; Office of Polar Programs","keywords":"Snowmelt; Mercury (programming language); Watershed; Environmental science; Arctic; Surface runoff; The arctic; Environmental chemistry; Hydrology (agriculture); Oceanography; Chemistry; Geology; 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":[0.0001380204,0.0002004545,0.0002319924,0.0005416026,0.0007879268,0.000544677,0.0002399432,0.0002284799,0.0004880118],"category_scores_gemma":[0.0002460786,0.0001528756,0.0001767841,0.0007254049,0.0002548881,0.0002708673,0.0003469496,0.0001711064,0.0000689754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032816,"about_ca_system_score_gemma":0.0007218132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06313596,"about_ca_topic_score_gemma":0.1228874,"domain_scores_codex":[0.9999086,0.000008338702,0.00000780806,0.00003030281,0.00003155652,0.00001328747],"domain_scores_gemma":[0.9998469,0.00002307884,0.00003446909,0.000007869542,0.00004489259,0.00004273289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001927786,0.0001505691,0.958318,0.00003245429,0.0000489975,0.0008328245,0.001580049,0.0008121306,0.0301901,0.00004184704,0.0002164838,0.00758384],"study_design_scores_gemma":[0.000005731674,0.00006743737,0.9935843,0.000007497267,0.00002517777,0.0001012007,0.001355851,0.00174677,0.00257791,0.0000424188,0.0004787806,0.000006905126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996083,0.00001002334,0.00003853542,0.00001068891,0.000001067939,0.00000260906,0.00008714148,0.000009103571,0.0002324487],"genre_scores_gemma":[0.9992864,0.00003546602,0.0002023467,0.00001289144,0.000002640566,0.000006469914,0.0002444568,0.000002996162,0.000206499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06313596,"threshold_uncertainty_score":0.1255369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01190515480780091,"score_gpt":0.2370733033493541,"score_spread":0.2251681485415532,"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."}}