{"id":"W4386227329","doi":"10.1016/j.dib.2023.109524","title":"DeepFruit: A dataset of fruit images for fruit classification and calories calculation","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Prince Mohammad Bin Fahd University","keywords":"Artificial intelligence; Normalization (sociology); Computer science; Preprocessor; Pattern recognition (psychology); Set (abstract data type); Digital image; Computer vision; Image (mathematics); Image processing","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":[0.0002849936,0.001936456,0.001047545,0.002226798,0.0005869357,0.0008309793,0.002310193,0.001742438,0.009394476],"category_scores_gemma":[0.001131081,0.0003937118,0.001176845,0.002357075,0.0003633964,0.0008710116,0.0009212514,0.001230696,0.009651115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194361,"about_ca_system_score_gemma":0.0009118649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02291509,"about_ca_topic_score_gemma":0.06050373,"domain_scores_codex":[0.9996038,0.00003001641,0.0000257633,0.0001533503,0.0001225901,0.0000644604],"domain_scores_gemma":[0.9996681,0.00004741601,0.00003173751,0.00007598526,0.0001372589,0.00003947902],"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":[0.0009442626,0.0006795894,0.01199961,0.002724589,0.0003191435,0.0007287461,0.0001293597,0.008973203,0.01847555,0.001318664,0.8165569,0.1371504],"study_design_scores_gemma":[0.0004357478,0.0005324773,0.1120622,0.0008450036,0.0002826014,0.002564965,0.000624749,0.09068841,0.04853695,0.005113584,0.7379035,0.000409872],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05317128,0.002712509,0.01206535,0.0004987875,0.0004140249,0.0003655636,0.9098826,0.01074025,0.01014961],"genre_scores_gemma":[0.03459142,0.0005219593,0.01806499,0.0002042904,0.000037852,0.0003017317,0.9418082,0.0003033092,0.004166299],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02291509,"threshold_uncertainty_score":0.0455634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08859679350197426,"score_gpt":0.301643437241672,"score_spread":0.2130466437396977,"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."}}