{"id":"W6968305503","doi":"10.5281/zenodo.16544448","title":"GROW-Africa (Groundtruthing Remote-sensing for Optimizing Yield in Africa) Database, v2.0","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Yield (engineering); Georeference; Crop; Agriculture; Crop production; Production (economics); Crop yield","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":[0.0007651005,0.00176253,0.0009084321,0.001719299,0.0004120429,0.001121165,0.002316747,0.001155376,0.01343972],"category_scores_gemma":[0.002364749,0.0004875551,0.0008287501,0.002999949,0.0002386029,0.00105269,0.001278932,0.0009641552,0.01964338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008341773,"about_ca_system_score_gemma":0.0009928207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02587682,"about_ca_topic_score_gemma":0.03594644,"domain_scores_codex":[0.9995499,0.00007570859,0.00006330444,0.0001247833,0.0001183329,0.00006799553],"domain_scores_gemma":[0.9995123,0.0001064135,0.00005987058,0.0001254968,0.0001469768,0.00004891623],"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":[0.0001158596,0.00005193635,0.003257681,0.00121365,0.00008484641,0.00008433141,0.00005215887,0.001991077,0.0007600442,0.0008491809,0.9825027,0.009036558],"study_design_scores_gemma":[0.00035107,0.00004090523,0.02026882,0.0003953911,0.0000522425,0.0001553403,0.0001992202,0.005032456,0.001634229,0.00208718,0.9697179,0.0000652697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006523354,0.0001026084,0.0002849348,0.00004626009,0.00001574561,0.00001850766,0.9979078,0.0005166391,0.0004552244],"genre_scores_gemma":[0.001129804,0.00005995889,0.0008131902,0.00002005301,0.000003096973,0.00007147079,0.9975711,0.00006218514,0.0002691303],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02587682,"threshold_uncertainty_score":0.0514524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04138313337836293,"score_gpt":0.2402119403760468,"score_spread":0.1988288069976839,"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."}}