{"id":"W2021608557","doi":"10.1021/es070147r","title":"Continuous Analysis of Dissolved Gaseous Mercury and Mercury Volatilization in the Upper St. Lawrence River:  Exploring Temporal Relationships and UV Attenuation","year":2007,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Mercury (programming language); Volatilisation; Environmental chemistry; Dissolved organic carbon; Chemistry; MERCURE; Surface water; Total organic carbon; Attenuation; Water column; Environmental science; Hydrology (agriculture); Analytical Chemistry (journal); Environmental engineering; Geology","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":[0.000178341,0.0002189912,0.0003513525,0.0003781834,0.0006207286,0.0006253532,0.0004097293,0.0003188833,0.0004125912],"category_scores_gemma":[0.0002548268,0.0001987593,0.0002162088,0.0009691603,0.0002384191,0.0002274073,0.0003071161,0.0003187562,0.0001157426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00248364,"about_ca_system_score_gemma":0.001753309,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4436282,"about_ca_topic_score_gemma":0.6874384,"domain_scores_codex":[0.9997652,0.00001411969,0.000008823799,0.00007850179,0.00009498238,0.00003831214],"domain_scores_gemma":[0.9997787,0.00002286782,0.00004721493,0.000008325273,0.0001195853,0.00002335686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002725889,0.0001071564,0.8532619,0.0001101338,0.0001050209,0.0001137996,0.001171193,0.0009478651,0.1230156,0.0001193541,0.0002784179,0.02049695],"study_design_scores_gemma":[0.000005494028,0.0001269313,0.9844439,0.000006803176,0.00003528043,0.00005253752,0.000429509,0.003681258,0.0101989,0.00002583738,0.0009816223,0.00001179731],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985288,0.00009146364,0.0004886095,0.00001216851,9.762435e-7,0.00001002146,0.0004083906,0.00002717809,0.0004323039],"genre_scores_gemma":[0.996722,0.00008988535,0.001574768,0.00001991372,0.000001420696,0.00002395164,0.0006754659,0.000006978279,0.000885637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5563718,"threshold_uncertainty_score":0.882092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602096304839594,"score_gpt":0.2537568304825041,"score_spread":0.2277358674341082,"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."}}