{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000146016,0.00007991161,0.0001524136,0.00004750845,0.0001383162,0.0000211939,0.00009897732,0.00003385906,0.00003785463],"category_scores_gemma":[0.0002033974,0.00007288513,0.0000182581,0.00009379019,0.00004054307,0.0001867703,0.0002760935,0.00005853191,0.00001200961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001653953,"about_ca_system_score_gemma":0.00002340211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000287846,"about_ca_topic_score_gemma":0.00006322647,"domain_scores_codex":[0.9993817,0.000008648985,0.0001338272,0.0002548847,0.00008734904,0.0001335536],"domain_scores_gemma":[0.9993774,0.0000793133,0.00002492467,0.0004356634,0.00004221379,0.00004052688],"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":[0.0002223059,0.000267231,0.0005675807,0.0006075707,0.0001089421,0.000005823774,0.00001131143,9.041165e-8,0.0001316563,0.0007688341,0.9942139,0.003094724],"study_design_scores_gemma":[0.0046357,0.0003163275,0.01042507,0.0002401319,0.0002906359,0.0000121781,0.0002388887,0.004665985,0.00008872354,0.002392153,0.9765776,0.0001165376],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01587928,0.008386074,0.02699218,0.0221438,0.0005158866,0.002423449,0.9220504,0.000122101,0.001486854],"genre_scores_gemma":[0.1128139,0.001147976,0.06319895,0.004842334,0.0003080278,0.0001297062,0.8174376,0.00001781042,0.0001037031],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1046127,"threshold_uncertainty_score":0.297217,"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."}}