{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008976661,0.0001174855,0.0001739389,0.00004222435,0.00007214295,0.00002811616,0.0001973701,0.00008524171,0.0001231525],"category_scores_gemma":[0.00003041853,0.0001130452,0.00003242188,0.0001093787,0.00005707332,0.0002208891,0.00010851,0.00009866942,0.0003300429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001496174,"about_ca_system_score_gemma":0.000006865041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01168614,"about_ca_topic_score_gemma":0.003845425,"domain_scores_codex":[0.998394,0.0004228776,0.0003835982,0.0002794088,0.0003060668,0.0002140576],"domain_scores_gemma":[0.9993743,0.00007774483,0.0001965493,0.0002609123,0.000006762715,0.00008370913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001228205,0.0001106719,0.9666334,0.00002784729,0.000006632656,6.522667e-7,0.002512546,0.01051755,0.01869883,0.0001304352,0.0002939921,0.001055194],"study_design_scores_gemma":[0.0008498959,0.0001211136,0.9522792,0.0001752703,0.00002211372,0.000001127103,0.001428496,0.03513999,0.007098854,0.0000791307,0.00249377,0.0003110813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954515,0.00001219773,0.001170975,0.00005514962,0.0001369456,0.0001554665,0.000008307961,0.00004165439,0.002967775],"genre_scores_gemma":[0.9986184,0.000001703117,0.001039732,0.0001580172,0.00005868137,0.0000112027,0.00003508969,0.00001310367,0.00006413503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02462244,"threshold_uncertainty_score":0.9948951,"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."}}