{"id":"W4381089830","doi":"10.1016/j.dib.2023.109322","title":"Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Banana Cultivation and Research","field":"Agricultural and Biological Sciences","cited_by":13,"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":"Crop; Fusarium wilt; Tanzania; Mycosphaerella; Cash crop; Fusarium oxysporum; Black spot; Fusarium oxysporum f.sp. cubense; Musaceae; Biology; Horticulture; Geography; Agronomy; Agriculture","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":[0.0004681772,0.001930038,0.000969152,0.002994127,0.0009565527,0.001013906,0.001860111,0.001678392,0.004504431],"category_scores_gemma":[0.00101402,0.0003890264,0.001358524,0.001891337,0.0005104956,0.0007320055,0.001144098,0.001069281,0.00382867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405394,"about_ca_system_score_gemma":0.0009878009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04181824,"about_ca_topic_score_gemma":0.1025511,"domain_scores_codex":[0.9993254,0.00004790929,0.00005799048,0.0002146648,0.0001975802,0.00015642],"domain_scores_gemma":[0.9994658,0.00007352685,0.00004670501,0.0001133652,0.000220773,0.00007990091],"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":[0.002466618,0.002410993,0.03367873,0.00548611,0.0005099822,0.006939976,0.0007351919,0.0111778,0.06022897,0.001285514,0.6514558,0.2236243],"study_design_scores_gemma":[0.000761632,0.001123507,0.3164102,0.002035306,0.0005164683,0.00986195,0.004151291,0.08464032,0.04706621,0.002177175,0.5308722,0.0003837403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.305601,0.007654455,0.006097067,0.001980447,0.001020261,0.001027702,0.6558927,0.008469816,0.01225663],"genre_scores_gemma":[0.1120441,0.001043165,0.01280107,0.0002781129,0.00009177094,0.0002806397,0.8691974,0.0001677423,0.004096063],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04181824,"threshold_uncertainty_score":0.08314967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229481043118909,"score_gpt":0.3413872244587279,"score_spread":0.218439120146837,"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."}}