{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002875801,0.00007214626,0.000118229,0.00001378889,0.00007067969,0.00003787762,0.0002497963,0.00005811551,0.00002187878],"category_scores_gemma":[0.0001555415,0.00002844879,0.00001420568,0.0003530255,0.00004914002,0.0002927209,0.0001547522,0.00004025296,0.000008599142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005466411,"about_ca_system_score_gemma":0.000003850848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005462306,"about_ca_topic_score_gemma":0.001404489,"domain_scores_codex":[0.9992684,0.00002308306,0.0001936408,0.0002717631,0.0001050587,0.000138019],"domain_scores_gemma":[0.9995144,0.0002291511,0.00006870017,0.0001241252,0.0000323762,0.00003125525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005312765,0.00008977197,0.1319134,0.00006007691,0.00001896276,0.000003490595,0.0001791583,0.000003448773,0.1886224,0.001484699,0.5923254,0.08524608],"study_design_scores_gemma":[0.00010877,0.00003477795,0.7585557,0.0000118095,0.000008518838,0.000001040068,0.0001885622,0.0003702314,0.0006214997,0.0002900024,0.2397335,0.00007557644],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965259,0.0001100659,0.00002358304,0.002072887,0.00007802544,0.0003448675,0.03202244,0.00004104389,0.00004810141],"genre_scores_gemma":[0.8686951,0.00009796394,0.0002045102,0.0001285798,0.0001856912,0.00003502684,0.1305734,8.267052e-7,0.00007888326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6266423,"threshold_uncertainty_score":0.1160108,"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."}}