{"id":"W4391822753","doi":"10.3390/foods13040568","title":"Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria","year":2024,"lang":"en","type":"article","venue":"Foods","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Federal Ministry of Agriculture and Rural Development, Nigeria; China Scholarship Council; State Key Laboratory of Resources and Environmental Information System; National Natural Science Foundation of China","keywords":"Food security; Land use; Land cover; Geospatial analysis; Agriculture; Suitability analysis; Agricultural land; Environmental science; Multiple-criteria decision analysis; Geography; Distribution (mathematics); Production (economics); Spatial distribution; Marginal land; Food processing; Agroforestry; Environmental protection; Environmental resource management; Remote sensing; Ecology","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":[0.0002192843,0.0001603755,0.0001405659,0.001963114,0.0003937587,0.0007491483,0.00009816968,0.0001739469,0.0009366558],"category_scores_gemma":[0.0008279142,0.000145564,0.0002119415,0.002014359,0.0002222754,0.0003157634,0.0004270725,0.00009743209,0.0001375267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004832542,"about_ca_system_score_gemma":0.0004034604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01620885,"about_ca_topic_score_gemma":0.03941121,"domain_scores_codex":[0.9998546,0.00003440036,0.00001877558,0.00002477548,0.00003656072,0.00003084958],"domain_scores_gemma":[0.999662,0.0001281918,0.00008226485,0.00001932164,0.00008135599,0.00002678528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009962226,0.00004055651,0.956966,0.00007366316,0.00003677095,0.000673035,0.0007691754,0.01403336,0.002903753,0.001117739,0.0003043558,0.02298203],"study_design_scores_gemma":[0.000004287462,0.00004763574,0.9575467,0.00004710672,0.00003117275,0.0004860676,0.005664285,0.03317212,0.0008235425,0.0006512297,0.001509021,0.00001691092],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964502,0.00007943278,0.0006807254,0.00002685046,0.000001873718,0.00001384941,0.0004694947,0.000008313119,0.002269323],"genre_scores_gemma":[0.9988601,0.00005175172,0.0007250586,0.00000132542,7.513453e-7,0.000008780771,0.000163176,0.000001018173,0.0001880429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01620885,"threshold_uncertainty_score":0.03222901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007157871914793048,"score_gpt":0.2580072560484847,"score_spread":0.2508493841336917,"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."}}