{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284281,0.0008584387,0.000399097,0.001616406,0.0005163852,0.0004352591,0.0010436,0.0009476063,0.004676768],"category_scores_gemma":[0.0008363469,0.0001520129,0.000625637,0.001552181,0.0003194411,0.000415846,0.0005900703,0.0006522963,0.00364346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009465633,"about_ca_system_score_gemma":0.000708635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0343511,"about_ca_topic_score_gemma":0.06254797,"domain_scores_codex":[0.9997419,0.00002442036,0.00002254843,0.00006705185,0.00007712279,0.00006705499],"domain_scores_gemma":[0.9996713,0.00004821101,0.00003511311,0.00007020149,0.0001289481,0.0000461665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001103454,0.001156284,0.03845618,0.002563644,0.0001931046,0.003450399,0.000554688,0.01158106,0.02781091,0.001762875,0.7721567,0.1392107],"study_design_scores_gemma":[0.0003845775,0.0004792261,0.2952141,0.000647556,0.0001511031,0.003432709,0.002822411,0.0503691,0.02490279,0.002076615,0.619337,0.0001829162],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2020578,0.001947345,0.003393082,0.001580355,0.0004020204,0.0005280252,0.7765705,0.003657081,0.009863776],"genre_scores_gemma":[0.1236461,0.0005145727,0.009069218,0.0001807247,0.00006000644,0.0002794379,0.8622351,0.00008027016,0.003934647],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0343511,"threshold_uncertainty_score":0.06830233,"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."}}