{"id":"W2953136989","doi":"10.5539/jsd.v12n4p1","title":"Geospatial Evaluation of Sustainable Development: Analysing a Sample of a Successful Social Safety Net","year":2019,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Sustainable Development and Environmental Policy","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Remote Sensing Centre","keywords":"Geospatial analysis; Poverty; Sustainable development; Business; Productivity; Government (linguistics); Environmental resource management; Environmental planning; Economic growth; Environmental economics; Economics; Geography; Political science; Remote sensing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.00120145,0.0002674438,0.000233964,0.002993104,0.001022506,0.001208254,0.0005042859,0.0003504984,0.002176989],"category_scores_gemma":[0.004580256,0.0001057867,0.0002651502,0.003653764,0.0008556682,0.0008328651,0.001216987,0.0002727013,0.0004665471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294398,"about_ca_system_score_gemma":0.000804002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01145781,"about_ca_topic_score_gemma":0.01744177,"domain_scores_codex":[0.9990085,0.0002802643,0.00006416125,0.00007725658,0.0004587425,0.0001110555],"domain_scores_gemma":[0.9974974,0.000777532,0.0004914462,0.0001612896,0.0007964836,0.0002758092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001845214,0.0006445044,0.8920772,0.0001718438,0.00007534024,0.001183798,0.0183897,0.003504025,0.002157294,0.003529426,0.001440013,0.0766423],"study_design_scores_gemma":[0.000005904077,0.0003457728,0.9080511,0.0000566937,0.00002028583,0.0002789508,0.07176917,0.01028784,0.001308546,0.001176349,0.006680444,0.00001901609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996405,0.0000224611,0.0006016565,0.00003747646,0.000001856429,0.00005826218,0.0002742107,0.000005964488,0.002593046],"genre_scores_gemma":[0.9966294,0.00004448904,0.001417273,0.000008830138,0.000001723513,0.00007857085,0.0005909912,0.000006725518,0.001221837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01145781,"threshold_uncertainty_score":0.02278221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123699914913808,"score_gpt":0.2445768603472048,"score_spread":0.2333398611980667,"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."}}