{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001198655,0.00009311779,0.0001887579,0.00006154936,0.0004122974,0.00003165655,0.0001311776,0.00002438026,0.0005131878],"category_scores_gemma":[0.00006547574,0.00008655547,0.000107659,0.0001950936,0.0000317878,0.00008171027,0.0001249218,0.0002031159,0.000009455847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007078898,"about_ca_system_score_gemma":0.00002461757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001929422,"about_ca_topic_score_gemma":0.00002459237,"domain_scores_codex":[0.9991757,0.00001724413,0.0001479452,0.0002069634,0.000226861,0.0002252552],"domain_scores_gemma":[0.9995639,0.0001125515,0.00005608457,0.0001045279,0.0001066083,0.00005631325],"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":[0.004588147,0.002504112,0.1296773,0.001210965,0.001489865,0.0002534248,0.0006616274,0.001373522,0.01426259,0.004936928,0.7621691,0.0768725],"study_design_scores_gemma":[0.007398576,0.00242108,0.04415363,0.00004333675,0.0002169087,0.0001121023,0.001321168,0.0005536273,0.004646545,0.00417125,0.9345601,0.0004017413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246864,0.005492064,0.01477526,0.01442443,0.002811595,0.001998244,0.0002685817,0.0003239969,0.03521949],"genre_scores_gemma":[0.9960596,0.00004929509,0.0006098269,0.001388353,0.0004633761,0.0003121317,0.00004523542,0.00002310902,0.001049079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.172391,"threshold_uncertainty_score":0.5619047,"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."}}