{"id":"W1833458028","doi":"10.3233/ajw-2010-7_1_06","title":"Assessing Vulnerability of the Arsenic Exposed Population in India","year":2010,"lang":"en","type":"article","venue":"Asian Journal of Water Environment and Pollution","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Arsenic; Vulnerability (computing); Population; Environmental science; Geography; Environmental health; Computer science; Computer security; Medicine; Materials science; 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":[0.0003294126,0.0002243,0.0001756144,0.001311021,0.0009459173,0.0009061443,0.0005133695,0.0004300929,0.001486795],"category_scores_gemma":[0.001121134,0.0001827661,0.0003490777,0.001042938,0.0004985137,0.0004569209,0.001624322,0.0004940712,0.0003012834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007676372,"about_ca_system_score_gemma":0.0008390141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02536921,"about_ca_topic_score_gemma":0.02628521,"domain_scores_codex":[0.9996004,0.0001040825,0.00003240699,0.00003707629,0.00009493547,0.0001311266],"domain_scores_gemma":[0.9994963,0.00009093697,0.0001335874,0.00002523009,0.0001271851,0.0001267728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007130143,0.0001699182,0.9607258,0.0001547087,0.00006538758,0.001442287,0.01807951,0.0003948343,0.001018574,0.000464402,0.001202544,0.01621079],"study_design_scores_gemma":[0.000002874539,0.0002292154,0.9607077,0.00005678595,0.00004250497,0.001149088,0.03405988,0.0004107307,0.000256631,0.0004475699,0.002608558,0.0000283948],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961078,0.0001568716,0.000108497,0.0003589013,0.000007438643,0.00003968743,0.0003579179,0.000008710991,0.002854133],"genre_scores_gemma":[0.9988278,0.0002823182,0.0001236691,0.0001007107,0.000006053606,0.00003369887,0.0001351782,0.000001494646,0.0004890835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02536921,"threshold_uncertainty_score":0.05044305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007546572056378787,"score_gpt":0.2181312765616292,"score_spread":0.2105847045052504,"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."}}