{"id":"W4413743190","doi":"10.18280/ijsdp.200712","title":"Integrating NTL Imagery and Environmental Indicators for Poverty Mapping in India: An Approach Toward SDG-1","year":2025,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Poverty; Environmental resource management; Environmental planning; Geography; Environmental science; Natural resource economics; Economics; Economic growth","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":[0.0004197136,0.0004532991,0.0002204459,0.001226469,0.0002770971,0.0009280886,0.0009549691,0.0003271056,0.0006828578],"category_scores_gemma":[0.0008225913,0.0002165127,0.0005141931,0.001331016,0.0003680942,0.0006598428,0.001447367,0.0005262378,0.0001887296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007911936,"about_ca_system_score_gemma":0.00132828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03251733,"about_ca_topic_score_gemma":0.0336083,"domain_scores_codex":[0.9997947,0.00006321424,0.00001011874,0.00004435453,0.00003923928,0.00004841902],"domain_scores_gemma":[0.9998031,0.00003951129,0.00003125821,0.00002719005,0.00007938231,0.00001954354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001980348,0.0003542594,0.1088459,0.0003497007,0.0002111851,0.0006155129,0.0006741333,0.4706365,0.00877842,0.01119544,0.006016971,0.392124],"study_design_scores_gemma":[0.000007936359,0.00006363977,0.02898112,0.00004670684,0.00004938908,0.000083901,0.0008388495,0.9594451,0.003136558,0.003910757,0.003405777,0.00003030523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6834172,0.0009680748,0.2950316,0.00327889,0.00009125484,0.0002197212,0.00231387,0.001283309,0.01339611],"genre_scores_gemma":[0.9423184,0.0003471675,0.05459407,0.0001216303,0.00002235501,0.00006165057,0.0008405609,0.00002511707,0.001669068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03251733,"threshold_uncertainty_score":0.06465608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091949566735068,"score_gpt":0.2313798290433709,"score_spread":0.2204603333760202,"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."}}