{"id":"W1651385589","doi":"10.4491/eer.2005.10.6.306","title":"IDENTIFICATION OF POSSIBLE MERCURY SOURCES AND ESTIMATION OF MERCURY WET DEPOSITION FLUX IN LAKE ONTARIO FROM LAKE ONTARIO ATMOSPHERIC DEPOSITION STUDY (LOADS)","year":2005,"lang":"en","type":"article","venue":"Environmental Engineering Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Deposition (geology); Environmental science; MERCURE; Flux (metallurgy); Hydrology (agriculture); Precipitation; Environmental chemistry; Meteorology; Geology; Chemistry; Geography; Geomorphology; Sediment; Analytical Chemistry (journal)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001640851,0.0002458601,0.000166273,0.0007016506,0.001330911,0.0005239584,0.0002670896,0.0001671332,0.0006034028],"category_scores_gemma":[0.000353306,0.0002122501,0.0001953934,0.00115986,0.0002060821,0.0001536211,0.0003354536,0.000111625,0.0001283099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007145948,"about_ca_system_score_gemma":0.005235781,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9597853,"about_ca_topic_score_gemma":0.9914891,"domain_scores_codex":[0.9998063,0.000009866283,0.00001070295,0.00003340351,0.00009829988,0.00004133958],"domain_scores_gemma":[0.9997784,0.00001148862,0.00006246091,0.000007132372,0.0001147138,0.00002574646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001181784,0.0000255767,0.9765621,0.00007979136,0.00004766332,0.0003137244,0.00227024,0.0002633817,0.01067606,0.00008608526,0.001021598,0.008535663],"study_design_scores_gemma":[0.000004620399,0.00001179711,0.9971221,0.000005184831,0.00001314682,0.0000275572,0.0003350217,0.000223298,0.000696513,0.000008129393,0.001549207,0.000003544972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960775,0.0001557419,0.0002127591,0.00006254371,0.000001797263,0.00003906912,0.001412929,0.00001127696,0.002026499],"genre_scores_gemma":[0.9927027,0.0003515726,0.001194198,0.00004788498,0.000003811224,0.00004908866,0.002239473,0.000007294068,0.003404034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04021472,"threshold_uncertainty_score":0.08090305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383303387769123,"score_gpt":0.255975228981826,"score_spread":0.2421421951041348,"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."}}