{"id":"W2553492492","doi":"10.1080/07900627.2016.1253543","title":"Development and application of a multi-scalar, participant-driven water poverty index in post-tsunami India","year":2016,"lang":"en","type":"article","venue":"International Journal of Water Resources Development","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Virginia Space Grant Consortium; U.S. Department of State","keywords":"Poverty; Index (typography); Human settlement; Water quality; Geography; Socioeconomics; Water resource management; Environmental planning; Economic growth; Environmental science; Economics; Computer science","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":[0.0004443741,0.0001782506,0.0002798272,0.0005355325,0.00005103078,0.0000612728,0.0004046914,0.00008471382,0.00005007812],"category_scores_gemma":[0.00002507575,0.00009375832,0.00005970617,0.00005115733,0.00006472517,0.0002985926,0.0001954363,0.0001352558,0.00002115345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430303,"about_ca_system_score_gemma":0.00002762093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002738562,"about_ca_topic_score_gemma":0.00007264203,"domain_scores_codex":[0.9977695,0.0000664846,0.001059103,0.0002117275,0.0006036254,0.0002895762],"domain_scores_gemma":[0.9991244,0.00003928316,0.0002552392,0.00009558088,0.0003693694,0.0001161479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002861038,0.001137603,0.554942,0.00009142746,0.0005579661,0.0001109171,0.09008702,0.0001043087,0.2302333,0.00001990874,0.0001037647,0.1197508],"study_design_scores_gemma":[0.003715977,0.00005387444,0.3839978,0.0003362461,0.00001399785,0.00009487283,0.0002179347,0.00008945288,0.5466517,0.00008387631,0.06453393,0.000210397],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993818,0.00004710929,0.00168753,0.003908559,0.0003181217,0.0001726924,0.00000546303,0.0000116322,0.00003086505],"genre_scores_gemma":[0.9957771,0.00001060354,0.003518864,0.000498613,0.00008537506,0.00001573261,0.00001687051,0.00001915882,0.00005770284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3164183,"threshold_uncertainty_score":0.3823353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02000647299933481,"score_gpt":0.2726272411206531,"score_spread":0.2526207681213183,"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."}}