{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005093503,0.00005625303,0.00008178587,0.00003589901,0.00005773341,0.00001248456,0.00006675146,0.00004816141,0.0003137168],"category_scores_gemma":[0.0000152845,0.00003471046,0.00003968078,0.00004024607,0.0001037848,0.00037375,0.00004008839,0.000164647,0.000004072453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006445335,"about_ca_system_score_gemma":0.000003627707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001696648,"about_ca_topic_score_gemma":0.00002863257,"domain_scores_codex":[0.9992818,0.00009037422,0.0002878974,0.00007415561,0.0001811599,0.00008456768],"domain_scores_gemma":[0.9996946,0.000005903767,0.0001751518,0.00008908319,0.000002201547,0.0000330098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009620052,0.00004586492,0.5023189,0.000002672971,0.000002921593,7.879102e-7,0.001560113,0.0001249795,0.4472731,0.0000580828,0.000006652429,0.04859633],"study_design_scores_gemma":[0.0002674412,0.00002462429,0.968478,0.000008746499,0.000008781129,0.00001359088,0.0002103114,0.0001020785,0.02987031,0.0007138624,0.0002606712,0.00004162472],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998309,0.000004603039,0.0001564906,0.0008046445,0.0001373165,0.00007827763,5.597137e-7,0.000001224276,0.0005079408],"genre_scores_gemma":[0.9995826,0.000004719517,0.000301124,0.00003134662,0.00002320925,7.027938e-7,0.000001690567,0.00000311872,0.00005150629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4661591,"threshold_uncertainty_score":0.3434978,"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."}}