{"id":"W2560655887","doi":"","title":"Arsenic Mobilization from Source Sediments in an Aquifer System of West Bengal, India","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Mobilization; BENGAL; Aquifer; West bengal; Indus; Geology; Arsenic; Water resource management; Geochemistry; Mining engineering; Environmental science; Oceanography; Groundwater; Geography; Archaeology; Socioeconomics; Geotechnical engineering; Geomorphology; Metallurgy","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":[0.00009508883,0.00025722,0.0002260685,0.0009344807,0.001850761,0.001138832,0.0005870261,0.0003962481,0.0006285935],"category_scores_gemma":[0.0002074323,0.0002364968,0.0002411335,0.001176572,0.0006081547,0.0004225399,0.0005854682,0.0002718274,0.0001771957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355719,"about_ca_system_score_gemma":0.001082421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1297387,"about_ca_topic_score_gemma":0.1256968,"domain_scores_codex":[0.9998667,0.0000184391,0.00001242847,0.00003473731,0.00002894522,0.00003871991],"domain_scores_gemma":[0.999891,0.000018088,0.00002187046,0.000006708632,0.00004596233,0.00001631153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001739383,0.0004182645,0.6792122,0.0004397349,0.0002271367,0.006653255,0.01523897,0.004797135,0.2672822,0.0009371237,0.0008662791,0.02218818],"study_design_scores_gemma":[0.00003001127,0.0003529913,0.9519935,0.00002125757,0.0001282041,0.0009783976,0.01435018,0.003253396,0.02612257,0.00029604,0.002437639,0.00003586081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991374,0.00001775803,0.00005667295,0.00002260404,0.000001530155,0.000004765167,0.0001049953,0.000005522232,0.0006487481],"genre_scores_gemma":[0.999386,0.00003734083,0.00007766364,0.000008425891,0.000001569224,0.000003225247,0.00007227086,0.000002235193,0.0004112302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1297387,"threshold_uncertainty_score":0.2579671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014548496521952,"score_gpt":0.2453762072357721,"score_spread":0.2308277107138201,"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."}}