{"id":"W4312959094","doi":"10.1109/access.2022.3227769","title":"DeepNOVA: A Deep Learning NOVA Classifier for Food Images","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Artificial intelligence; Classifier (UML); Computer science; Deep learning; Homogeneous; Novel food; Pattern recognition (psychology); Machine learning; Object detection; Food science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0008304818,0.001550607,0.001147023,0.00128421,0.0004881016,0.001127263,0.00240322,0.001746272,0.005791015],"category_scores_gemma":[0.002006623,0.0005640392,0.001393397,0.0008700391,0.0003236194,0.001487189,0.001473911,0.002001049,0.003939346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001196091,"about_ca_system_score_gemma":0.0007930195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007126069,"about_ca_topic_score_gemma":0.01412487,"domain_scores_codex":[0.9996141,0.00005347303,0.00002572524,0.0001530373,0.00008861411,0.00006509024],"domain_scores_gemma":[0.9995166,0.0001287813,0.00004924719,0.00008539359,0.0001746282,0.00004534709],"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":[0.000766033,0.0008928074,0.009961408,0.0004084179,0.0004109287,0.0003035612,0.00006388093,0.06459807,0.01608449,0.002646002,0.04784375,0.8560207],"study_design_scores_gemma":[0.00003052633,0.0002029837,0.002643873,0.00006178224,0.00005775982,0.0001751106,0.00003330329,0.9762466,0.008708741,0.003416069,0.008391355,0.00003193377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1669876,0.00578631,0.7671607,0.001410241,0.001376619,0.0005010273,0.01210905,0.02743982,0.01722868],"genre_scores_gemma":[0.5194144,0.001880344,0.4108959,0.001908502,0.0002563023,0.0005193032,0.03254835,0.000702453,0.03187444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007126069,"threshold_uncertainty_score":0.01937288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07799363206883118,"score_gpt":0.3518048643333309,"score_spread":0.2738112322644997,"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."}}