{"id":"W4409766480","doi":"10.1038/s41597-025-05001-z","title":"HarvestStat Africa – Harmonized Subnational Crop Statistics for Sub-Saharan Africa","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"U.S. Geological Survey; Bundesministerium für Bildung und Forschung; Goddard Space Flight Center; Wyoming-Montana Water Science Center; Volkswagen Foundation; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig; Consortium of International Agricultural Research Centers; U.S. Department of the Interior; Canada Research Chairs; United States Agency for International Development; Deutsche Forschungsgemeinschaft; National Aeronautics and Space Administration","keywords":"Agriculture; Sorghum; Famine; Agricultural productivity; Yield (engineering); Agricultural economics; Scarcity; Geography; Crop; Crop yield; Agroforestry; Environmental science; Economics; Ecology; Forestry; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001300166,0.0008950914,0.0005519807,0.004798015,0.0002859838,0.001080066,0.0008824521,0.000348968,0.00927075],"category_scores_gemma":[0.006090579,0.0003921681,0.0004299638,0.01288929,0.0001805969,0.001219114,0.001101667,0.0005707273,0.004317473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007326754,"about_ca_system_score_gemma":0.002156095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0244314,"about_ca_topic_score_gemma":0.01463198,"domain_scores_codex":[0.9992406,0.0001543028,0.0001751768,0.0001344516,0.0001793197,0.0001161517],"domain_scores_gemma":[0.9964799,0.0005845421,0.001177933,0.0004667301,0.001095363,0.0001954976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000948085,0.0001206587,0.1094107,0.00394215,0.0004249904,0.0006035223,0.001035099,0.006137937,0.003251305,0.009890896,0.7518494,0.1123852],"study_design_scores_gemma":[0.0001530813,0.00003392595,0.1477765,0.0005714155,0.00006362584,0.0001712412,0.0005996982,0.001654031,0.001576871,0.001888656,0.8454561,0.00005483614],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01181136,0.0003432512,0.001482469,0.0001770208,0.00006354455,0.00009150443,0.981748,0.0004200578,0.00386271],"genre_scores_gemma":[0.02593311,0.0007233677,0.006315086,0.0001042268,0.00004224268,0.0006146813,0.9638596,0.0002923325,0.00211536],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0244314,"threshold_uncertainty_score":0.04857838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283449131331734,"score_gpt":0.3038879219948393,"score_spread":0.1755430088616659,"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."}}