{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003440876,0.00006977833,0.0001400694,0.00002061793,0.00002716648,0.00002030875,0.00009236154,0.00005777467,0.001057872],"category_scores_gemma":[0.00002356995,0.00004762356,0.0001143178,0.0001904099,0.00005700661,0.00007018209,0.00005213934,0.00006746209,0.000009292016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007966448,"about_ca_system_score_gemma":0.00001336987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001666468,"about_ca_topic_score_gemma":0.009070851,"domain_scores_codex":[0.9992638,0.00004033607,0.0001773361,0.0002209531,0.0001583641,0.0001391925],"domain_scores_gemma":[0.999762,0.00005754307,0.00001818443,0.0001232043,0.000005188681,0.00003386175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001984307,0.0001544925,0.9826736,0.00007460852,0.00002709121,0.000003183976,0.0003752577,0.003023327,0.002664902,0.00005094406,0.0005329557,0.01039978],"study_design_scores_gemma":[0.0002018132,0.0001028694,0.9723266,0.000009684325,0.00002449912,0.000001026746,0.00009539762,0.02284506,0.0003441333,0.001430958,0.002525093,0.00009280029],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935653,0.0000531767,0.003672597,0.0002522525,0.000157717,0.0001563929,0.00004090471,0.00001792711,0.002083703],"genre_scores_gemma":[0.9991171,0.000003555991,0.0005792351,0.00001268554,0.0000490842,0.00002624945,0.00002435558,0.000003530287,0.0001842378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01982173,"threshold_uncertainty_score":0.9998553,"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."}}