{"id":"W4402306822","doi":"10.18280/ts.410425","title":"NutriFoodNet: A High-Accuracy Convolutional Neural Network for Automated Food Image Recognition and Nutrient Estimation","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Image (mathematics); Artificial neural network; Estimation; Nutrient; Computer vision; Biology; Ecology; Engineering","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":[0.0004830477,0.001731105,0.0006257318,0.001224064,0.000297426,0.0005501591,0.00172821,0.0009111215,0.003570178],"category_scores_gemma":[0.0009437996,0.0005093475,0.0007486316,0.0008833248,0.0002579779,0.00139732,0.0008603245,0.0008220707,0.002179204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097344,"about_ca_system_score_gemma":0.001014946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01840642,"about_ca_topic_score_gemma":0.02717106,"domain_scores_codex":[0.9997677,0.0000190959,0.00001418589,0.00008367962,0.00007712846,0.00003825882],"domain_scores_gemma":[0.9997973,0.00004404344,0.00002793517,0.00003319637,0.00008378974,0.00001387323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008729637,0.0006817363,0.009661851,0.0006169885,0.0004647663,0.00054354,0.00007437612,0.1004991,0.03822876,0.002856728,0.07302531,0.7724739],"study_design_scores_gemma":[0.00005083021,0.0003194312,0.004599317,0.00005780817,0.00008386705,0.0002551891,0.00003281852,0.9495503,0.02635681,0.002090328,0.01655079,0.00005254697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2287198,0.007632783,0.6420721,0.0009595632,0.00110015,0.0008359504,0.01709446,0.07865524,0.02293009],"genre_scores_gemma":[0.4938664,0.002765163,0.4136055,0.001081896,0.0001561314,0.0008086726,0.0572662,0.0008391808,0.02961085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01840642,"threshold_uncertainty_score":0.03659856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03214318137656538,"score_gpt":0.2799431242155866,"score_spread":0.2477999428390212,"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."}}