{"id":"W2026967510","doi":"10.1016/j.envres.2008.12.001","title":"Atmospheric mercury in Changbai Mountain area, northeastern China I. The seasonal distribution pattern of total gaseous mercury and its potential sources","year":2009,"lang":"en","type":"article","venue":"Environmental Research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":117,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Chinese Academy of Sciences; Ontario Innovation Trust","keywords":"Environmental science; Mercury (programming language); Seasonality; Morning; Atmospheric sciences; Daytime; Wind speed; Diurnal temperature variation; Diel vertical migration; Climatology; Meteorology; Geography; Ecology; Oceanography; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007259405,0.0001877982,0.0001961781,0.00001835391,0.0003185515,0.00003538401,0.0002068106,0.00006895082,0.001396858],"category_scores_gemma":[0.00003712009,0.0001415147,0.00005943455,0.0002372423,0.0005120222,0.0002229423,0.0003441992,0.0003425801,0.0001236252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002303851,"about_ca_system_score_gemma":0.000009540211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004719444,"about_ca_topic_score_gemma":0.0000980246,"domain_scores_codex":[0.997561,0.0002594429,0.0002648352,0.0003557411,0.001017762,0.00054118],"domain_scores_gemma":[0.999486,0.00007826959,0.0000749715,0.0002056417,0.000004075563,0.0001510068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002340092,0.0008775441,0.6414225,0.00002692512,0.00005316045,0.00007932852,0.006804369,0.001680578,0.06826168,0.00002808861,0.0004693649,0.2800625],"study_design_scores_gemma":[0.0004075531,0.0002680047,0.9890043,0.00002187183,0.000010069,0.00004180771,0.001580988,0.006122062,0.001963177,0.0001724655,0.0002468333,0.0001608272],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970645,0.0009049968,0.00009387649,0.001071594,0.00003303194,0.0004077884,0.0001169009,0.000009310489,0.0002980189],"genre_scores_gemma":[0.9988729,0.0004229177,0.000009376379,0.00006502566,0.00004723301,0.00002583726,0.0000605138,0.00001143284,0.000484739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3475819,"threshold_uncertainty_score":0.999516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667397238257406,"score_gpt":0.2718353705514662,"score_spread":0.2551613981688922,"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."}}