{"id":"W3005797892","doi":"10.1109/access.2020.2973625","title":"DeepFood: Food Image Analysis and Dietary Assessment via Deep Model","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Pattern recognition (psychology); Feature (linguistics); Artificial neural network; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244176,0.001251428,0.0005799861,0.0006130218,0.0001779062,0.0005442747,0.001266029,0.0008009814,0.003465844],"category_scores_gemma":[0.0005747224,0.0003870649,0.0008114672,0.0006732306,0.0001695974,0.00125831,0.000890088,0.0009694462,0.001606122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007260622,"about_ca_system_score_gemma":0.0006073696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01337505,"about_ca_topic_score_gemma":0.01977627,"domain_scores_codex":[0.9998567,0.0000146622,0.00000534918,0.00006170991,0.00003561438,0.00002584671],"domain_scores_gemma":[0.9998859,0.00002385688,0.00001660928,0.00002248296,0.00003979426,0.00001138271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006615655,0.0005492936,0.008489564,0.0003158003,0.0003713852,0.0003549802,0.00008670778,0.1064642,0.03059335,0.002139359,0.03441579,0.815558],"study_design_scores_gemma":[0.00002266539,0.00009974123,0.002296648,0.00002306627,0.00004699102,0.0001002239,0.00002768341,0.9824333,0.007849237,0.00296084,0.004115539,0.00002396142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.127037,0.003241244,0.8258075,0.0008030297,0.0003385381,0.0002072991,0.007681862,0.02634159,0.008541924],"genre_scores_gemma":[0.5597566,0.002074845,0.3951891,0.001150373,0.0001029491,0.0003273098,0.02038565,0.0005732169,0.02043993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01337505,"threshold_uncertainty_score":0.0265944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05435476676601119,"score_gpt":0.3489701317059993,"score_spread":0.2946153649399881,"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."}}