{"id":"W3139966022","doi":"","title":"Human exposure to mercury as a consequence of landscape management and socio-economical behaviors： Three case studies in Canada","year":2006,"lang":"en","type":"article","venue":"中国地球化学学报：英文版","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Environmental planning; Environmental resource management; Business; Natural resource economics; Environmental science; Geography; Computer science; Economics","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":[0.0005884473,0.0004156239,0.0004418923,0.00110019,0.007759486,0.001201018,0.001166203,0.0009174315,0.0009998935],"category_scores_gemma":[0.0009577218,0.0003826179,0.0005318444,0.003532487,0.001918031,0.0003078496,0.001073052,0.0007699197,0.0001169086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04616899,"about_ca_system_score_gemma":0.02474136,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.996581,"about_ca_topic_score_gemma":0.9986827,"domain_scores_codex":[0.9992334,0.0001146874,0.00002318584,0.00007188124,0.0002052578,0.0003515937],"domain_scores_gemma":[0.9992404,0.00009840148,0.0001031302,0.00002368867,0.0003442858,0.0001901442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009465724,0.0007916542,0.879161,0.00033731,0.0002578645,0.009686682,0.05764268,0.002465509,0.003907728,0.001829694,0.002529309,0.0404439],"study_design_scores_gemma":[0.00004994597,0.0004270275,0.9177199,0.00008333515,0.0001640207,0.001446663,0.07041788,0.001468004,0.001321353,0.0002674508,0.006542645,0.00009183308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976251,0.0002867724,0.00006685752,0.0001580601,0.000003088239,0.00003880895,0.0001745929,0.000003241006,0.001643548],"genre_scores_gemma":[0.9964893,0.0005459669,0.0002265948,0.00008411014,0.000002045293,0.000013391,0.0001361237,0.000002441996,0.002500092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04616899,"threshold_uncertainty_score":0.334981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560964275346901,"score_gpt":0.2831209069995978,"score_spread":0.2575112642461288,"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."}}