{"id":"W2075164759","doi":"10.1016/j.atmosenv.2013.11.050","title":"A Great Lakes Atmospheric Mercury Monitoring network: Evaluation and design","year":2013,"lang":"en","type":"article","venue":"Atmospheric Environment","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Wisconsin Department of Natural Resources; Ministry of Environment; U.S. Environmental Protection Agency","keywords":"Mercury (programming language); Environmental science; Deposition (geology); Environmental monitoring; Air monitoring; Physical geography; Hydrology (agriculture); Geography; Geology; Environmental engineering; Sediment","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000469342,0.0002697493,0.0002212909,7.895522e-7,0.0003100432,0.00006052258,0.0001303742,0.00007515565,0.01231641],"category_scores_gemma":[0.00003031669,0.0002349897,0.00004817084,0.0001558172,0.0002272151,0.0004193661,0.0002133312,0.000106348,0.001740444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002847652,"about_ca_system_score_gemma":0.000007706908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002519455,"about_ca_topic_score_gemma":0.000003522014,"domain_scores_codex":[0.9980567,0.0001697481,0.0002833074,0.0004309192,0.0005813861,0.0004779567],"domain_scores_gemma":[0.9992705,0.0001003599,0.0001223624,0.0003015403,0.00000696615,0.0001982861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000143421,0.00008294945,0.6064652,0.00001074515,0.00009136884,0.000003856144,0.001229489,0.1078672,0.003241337,0.00001321755,0.01340073,0.2675796],"study_design_scores_gemma":[0.000655464,0.0001852097,0.9385222,0.00002499159,0.0001614951,0.00001294253,0.0005051558,0.04350976,0.0002754378,0.001159823,0.01446151,0.0005260671],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812474,0.002253253,0.01074245,0.0003577023,0.0002288423,0.001208838,6.435857e-7,0.00007042749,0.00389039],"genre_scores_gemma":[0.923259,0.001505091,0.07231332,0.0002205972,0.0001516553,0.0005387015,0.000003288915,0.0000369126,0.001971429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3320569,"threshold_uncertainty_score":0.9990368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492660257648581,"score_gpt":0.2450030897069837,"score_spread":0.2200764871304979,"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."}}