{"id":"W313217159","doi":"10.1007/978-94-017-9541-8_10","title":"Tracking Long-range Atmospheric Transport of Contaminants in Arctic Regions Using Lake Sediments","year":2015,"lang":"en","type":"book-chapter","venue":"Developments in paleoenvironmental research","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Environmental science; Deposition (geology); Arctic; Mercury (programming language); Sediment; Climate change; Environmental chemistry; Oceanography; Earth science; Geology; Chemistry","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":[0.0002427387,0.0003201624,0.0001675537,0.0005726686,0.0003915122,0.001179073,0.0002885924,0.0004038221,0.0009427656],"category_scores_gemma":[0.0001793112,0.0002454331,0.0003054019,0.001581427,0.0001094111,0.0008034127,0.0004701089,0.0002377273,0.00052948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004860608,"about_ca_system_score_gemma":0.0005105503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03328446,"about_ca_topic_score_gemma":0.1044284,"domain_scores_codex":[0.9999355,0.000004686627,0.000003079233,0.00002289969,0.00002756602,0.000006309157],"domain_scores_gemma":[0.999963,0.000008895054,0.000006028512,0.000002897401,0.00001623793,0.000002902758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009261805,0.00005031576,0.1060407,0.0004022052,0.0002191787,0.0002546296,0.000921638,0.01753695,0.1039257,0.005993789,0.01509677,0.7494656],"study_design_scores_gemma":[0.00001281374,0.0002272238,0.3796968,0.0006041832,0.000524293,0.0009552781,0.001522242,0.06538293,0.1612312,0.01454841,0.3750702,0.0002244884],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6747137,0.0487157,0.1420357,0.002165045,0.0007378869,0.00006339677,0.0137139,0.002120005,0.1157346],"genre_scores_gemma":[0.6767698,0.05427239,0.1647831,0.0004440457,0.0001835742,0.00005483921,0.007873181,0.0005037123,0.09511545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03328446,"threshold_uncertainty_score":0.06618142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1504790881985429,"score_gpt":0.3574186368827738,"score_spread":0.2069395486842308,"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."}}