{"id":"W7131894742","doi":"10.15353/jcvis.v8i1.5363","title":"Foodverse: a dataset of 3D food models for nutritional intake estimation","year":2023,"lang":"en","type":"article","venue":"NPARC","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Estimation; Quality (philosophy); Tracking (education); Conjunction (astronomy); Food intake","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0003911786,0.001872503,0.0009179902,0.001622292,0.00040518,0.0007838316,0.001456759,0.001704584,0.008291739],"category_scores_gemma":[0.001935946,0.0004472018,0.001822367,0.002110644,0.0003419394,0.0006689793,0.001292708,0.0009263221,0.007059529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000522473,"about_ca_system_score_gemma":0.000597926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01487485,"about_ca_topic_score_gemma":0.04617896,"domain_scores_codex":[0.9996307,0.00007964894,0.00003436869,0.0001273809,0.00009644151,0.00003155031],"domain_scores_gemma":[0.9995179,0.000127567,0.00005373442,0.0001293119,0.0001215355,0.00004996795],"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":[0.00229214,0.001177456,0.1208894,0.004919151,0.001602362,0.001811262,0.0006965365,0.02762561,0.01723357,0.002162012,0.5815352,0.2380553],"study_design_scores_gemma":[0.0005524642,0.001033201,0.2820225,0.001102252,0.0007042068,0.004614127,0.001774894,0.09888062,0.01372642,0.008450502,0.5865993,0.0005395619],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0927754,0.00271444,0.03186469,0.0004852758,0.0002984749,0.0003341846,0.8581564,0.006034715,0.007336393],"genre_scores_gemma":[0.08879233,0.0009854032,0.03949042,0.0002648888,0.00004017472,0.0005908848,0.8667503,0.0003674473,0.002718166],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01487485,"threshold_uncertainty_score":0.02957654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066147978497926,"score_gpt":0.3145610735263614,"score_spread":0.2484130950284354,"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."}}