{"id":"W4312220516","doi":"10.1016/j.sciaf.2022.e01518","title":"Food poverty assessment in Ghana: A closer look at the spatial and temporal dimensions of poverty","year":2022,"lang":"en","type":"article","venue":"Scientific African","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic Research Service; Ontario Ministry of Food and Agriculture; U.S. Department of Agriculture","keywords":"Poverty; Vulnerability (computing); Inequality; Economics; Population; Development economics; Geography; Panel data; Poverty rate; Socioeconomics; Demographic economics; Economic growth; Econometrics; Demography; Sociology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007739069,0.0001944107,0.0002571601,0.0009854448,0.00031922,0.0008745152,0.0002550485,0.0003817549,0.001466521],"category_scores_gemma":[0.002036745,0.0001345781,0.0002560931,0.001857505,0.0004162351,0.001218071,0.0008360174,0.0003704039,0.00007855451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001597307,"about_ca_system_score_gemma":0.001172749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04443389,"about_ca_topic_score_gemma":0.05064506,"domain_scores_codex":[0.9996902,0.0002000527,0.00001451203,0.00002239332,0.00003310187,0.00003978112],"domain_scores_gemma":[0.9995437,0.0002167753,0.0001252167,0.00001499113,0.00006725045,0.00003203464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002206727,0.0001392995,0.6988752,0.0003870762,0.0001471263,0.002770419,0.00638846,0.1392366,0.002358355,0.03108445,0.005055443,0.1133369],"study_design_scores_gemma":[0.00003036834,0.0003366318,0.5051696,0.0006700436,0.00009904594,0.001676326,0.04306998,0.3895327,0.001009062,0.02953677,0.02877738,0.00009216026],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9644702,0.002617523,0.01659148,0.005742104,0.00004107414,0.00005963079,0.0007045857,0.00003488664,0.009738517],"genre_scores_gemma":[0.9938341,0.001089813,0.004239121,0.00006195113,0.000008452366,0.00002124768,0.0001183947,0.000006796676,0.0006201843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04443389,"threshold_uncertainty_score":0.08835053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990086903227613,"score_gpt":0.2990472655829038,"score_spread":0.2691463965506277,"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."}}