{"id":"W7130708632","doi":"10.5281/zenodo.18715204","title":"Satellite Imagery and AI in Land Use Mapping and Monitoring in Nigeria","year":2000,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Satellite imagery; Land use; Land cover; Convolutional neural network; Satellite; Leverage (statistics); Natural resource; Agricultural land","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":[0.0005482229,0.0003696069,0.0001700453,0.000917099,0.0002412746,0.000538172,0.000233237,0.0003460521,0.000466506],"category_scores_gemma":[0.00107599,0.0002429774,0.0001467838,0.001228866,0.0002314043,0.0005675064,0.000349364,0.0003033974,0.000149584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005424406,"about_ca_system_score_gemma":0.0005214409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04747853,"about_ca_topic_score_gemma":0.072689,"domain_scores_codex":[0.9997688,0.00008941831,0.0000189727,0.00005106888,0.00004457957,0.00002720639],"domain_scores_gemma":[0.9997397,0.0001045129,0.00004751926,0.00002149551,0.00006616645,0.00002063331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000470478,0.0003609976,0.5563739,0.0004196841,0.0001324794,0.0009819448,0.0008956317,0.114283,0.01113828,0.002501921,0.002658662,0.3097832],"study_design_scores_gemma":[0.00003221634,0.0001632655,0.3703744,0.0003178169,0.0001085649,0.0004805275,0.002870172,0.6030366,0.01365882,0.001623009,0.007278207,0.00005640851],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876364,0.0009541647,0.005763837,0.0003330689,0.00003779681,0.00005952957,0.0007403857,0.00008987797,0.004385063],"genre_scores_gemma":[0.9846058,0.0006075958,0.01324452,0.00002212967,0.000008309032,0.00002080294,0.000513943,0.0000068267,0.0009699547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04747853,"threshold_uncertainty_score":0.09440434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181436906769003,"score_gpt":0.216190072015933,"score_spread":0.194375702948243,"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."}}