{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004394756,0.0003980975,0.000302812,0.002596412,0.0007836123,0.001897682,0.001064244,0.0002780864,0.0008664349],"category_scores_gemma":[0.008115384,0.0002690976,0.0004891885,0.002761264,0.0006944464,0.001067148,0.003130453,0.0006494577,0.0001615973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002348806,"about_ca_system_score_gemma":0.004730519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01938879,"about_ca_topic_score_gemma":0.03148618,"domain_scores_codex":[0.9968663,0.001753436,0.0002840666,0.0002317454,0.0006056892,0.0002588255],"domain_scores_gemma":[0.9963372,0.001319307,0.0005548849,0.0003405731,0.001177534,0.0002704917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004100931,0.0006306955,0.6247128,0.0006569138,0.0001357963,0.0006431803,0.048745,0.00466668,0.004894505,0.006172099,0.001924563,0.3064078],"study_design_scores_gemma":[0.00004480358,0.001978399,0.8683357,0.0003261602,0.0001428594,0.0005861521,0.08956385,0.011989,0.007690045,0.003633138,0.01556485,0.0001450637],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763891,0.00007165629,0.01306836,0.0002148417,0.00001713888,0.001337256,0.00101494,0.00009015923,0.007796618],"genre_scores_gemma":[0.9493289,0.0001875014,0.04614311,0.00004146923,0.000006608275,0.002626176,0.0009012625,0.00002660089,0.0007384758],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01938879,"threshold_uncertainty_score":0.03855187,"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."}}