{"id":"W4415864397","doi":"10.3390/data10110180","title":"NutritionVerse3D2D: Large 3D Object and 2D Image Food Dataset for Dietary Intake Estimation","year":2025,"lang":"en","type":"article","venue":"Data","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"National Research Council Sri Lanka","keywords":"Benchmark (surveying); Viewpoints; Process (computing); Food intake; Object (grammar); Pairwise comparison; Image (mathematics); Face (sociological concept)","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":[0.0004787782,0.002000672,0.001056013,0.001717145,0.0005050195,0.0007914689,0.001905747,0.001579445,0.006815935],"category_scores_gemma":[0.00189452,0.0005663969,0.001496021,0.001864119,0.0004337636,0.0007244926,0.001536551,0.001213152,0.006059227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005524295,"about_ca_system_score_gemma":0.0007982612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01234835,"about_ca_topic_score_gemma":0.04022884,"domain_scores_codex":[0.9995516,0.00006690081,0.00003252504,0.0001546955,0.0001518994,0.00004233521],"domain_scores_gemma":[0.9994389,0.0001181561,0.00006707368,0.0001744341,0.0001467544,0.00005462712],"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":[0.002019817,0.001632252,0.07518754,0.005159552,0.001210809,0.001625651,0.0005595006,0.02141968,0.04395035,0.002536828,0.6046343,0.2400638],"study_design_scores_gemma":[0.000630367,0.0008875639,0.2140553,0.001053765,0.0005357335,0.004324594,0.001105131,0.1056872,0.04032769,0.00915217,0.621675,0.0005654778],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08453601,0.002811036,0.06504967,0.0006918248,0.0005432329,0.0005417584,0.8195954,0.01818181,0.008049265],"genre_scores_gemma":[0.07198574,0.0009190403,0.08751065,0.0003257399,0.00005924506,0.0008740734,0.8349111,0.0009304491,0.002484073],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01234835,"threshold_uncertainty_score":0.02455294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04826100366341042,"score_gpt":0.3437124788237378,"score_spread":0.2954514751603274,"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."}}