{"id":"W4391012765","doi":"10.48550/arxiv.2401.08598","title":"NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Metadata; Estimation; Computer science; Segmentation; Ingredient; Artificial intelligence; World Wide Web; Food science; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0004928882,0.001340708,0.0008161036,0.002016499,0.0004535338,0.0007464553,0.001218907,0.001419724,0.01151507],"category_scores_gemma":[0.00202396,0.0004627822,0.0009690353,0.002248922,0.0003122268,0.000767856,0.001188811,0.0007407791,0.009024935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005893976,"about_ca_system_score_gemma":0.0007508222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01203242,"about_ca_topic_score_gemma":0.04666177,"domain_scores_codex":[0.9995629,0.00007878987,0.00003166841,0.000166652,0.0001121035,0.00004781368],"domain_scores_gemma":[0.9994814,0.0001235135,0.00006258531,0.0001260775,0.0001545556,0.00005196606],"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":[0.00233766,0.000927207,0.05371797,0.005220859,0.0008761114,0.0008225888,0.0006817784,0.005857223,0.03724986,0.002267081,0.702879,0.1871627],"study_design_scores_gemma":[0.00049588,0.00033865,0.1664636,0.0007352831,0.000296817,0.00144195,0.0008832103,0.02487177,0.01657803,0.006568923,0.7809852,0.0003405563],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04027369,0.001622929,0.02868132,0.0003665083,0.0002558212,0.0003029209,0.9053814,0.01429555,0.008819807],"genre_scores_gemma":[0.04064459,0.00061643,0.06025682,0.0002333641,0.00004216526,0.0006098076,0.8943429,0.001126548,0.002127364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9987811,"threshold_uncertainty_score":0.03852177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3282916296917724,"score_gpt":0.3277679401969341,"score_spread":0.0005236894948383308,"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."}}