{"id":"W4362474494","doi":"10.1016/j.dib.2023.109108","title":"Machine Learning Imagery Dataset for Maize Crop: A Case of Tanzania","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Styrelsen för Internationellt Utvecklingssamarbete; Makerere University; Harbin University of Science and Technology; International Development Research Centre; Rockefeller Foundation","keywords":"Tanzania; Food security; Cash crop; Crop; Staple food; Streak; Segmentation; Computer science; Artificial intelligence; Agricultural engineering; Geography; Agronomy; Biology; Agriculture; Environmental planning; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004031671,0.00008723196,0.0001502001,0.00001297949,0.0000958052,0.00003486906,0.0003702575,0.00004818477,0.0001669357],"category_scores_gemma":[0.0002235175,0.00003166177,0.00002767219,0.0004684306,0.00003247452,0.000192999,0.0003328029,0.00009180891,0.00003552811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004602628,"about_ca_system_score_gemma":0.000003643041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002565946,"about_ca_topic_score_gemma":0.00403741,"domain_scores_codex":[0.9991851,0.0000379345,0.0001902324,0.000291282,0.00008482434,0.0002105555],"domain_scores_gemma":[0.9994396,0.0002966109,0.00006367405,0.0001372014,0.00002092987,0.00004199444],"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":[0.0000895752,0.0001623796,0.02124209,0.00005849933,0.00002695849,0.0009759067,0.00009374598,0.00001649757,0.1226501,0.0002153375,0.792208,0.06226088],"study_design_scores_gemma":[0.0002711582,0.0001109804,0.04158226,0.00002204196,0.00001412653,0.0001438543,0.0002210609,0.001110226,0.0005864176,0.0001185262,0.955652,0.0001673254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9257402,0.0001499633,0.00000409418,0.001170361,0.00009910669,0.0002430513,0.07243327,0.0000535363,0.0001063781],"genre_scores_gemma":[0.753333,0.0001048669,0.0002650267,0.0003494423,0.0003637735,0.00002613895,0.2451584,0.000001707674,0.0003975455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1727252,"threshold_uncertainty_score":0.3878961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04853702762985089,"score_gpt":0.2802699819906373,"score_spread":0.2317329543607864,"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."}}