{"id":"W3091409978","doi":"10.2196/15602","title":"Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study","year":2020,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"EIT Food; European Commission","keywords":"Upload; Computer science; Artificial intelligence; Nutrition Labeling; Variety (cybernetics); Image (mathematics); World Wide Web; Medicine; Environmental health","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":[0.007896888,0.0007096379,0.0006375333,0.001682918,0.0003638543,0.001108696,0.0005576127,0.0007514191,0.00234501],"category_scores_gemma":[0.01897185,0.0002788555,0.000893401,0.0009001065,0.0004287613,0.001325056,0.0008733327,0.0003810286,0.0009994803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005241505,"about_ca_system_score_gemma":0.0003949958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002492084,"about_ca_topic_score_gemma":0.003435902,"domain_scores_codex":[0.9961151,0.001933928,0.0004193304,0.0005390513,0.0007981107,0.0001945181],"domain_scores_gemma":[0.9828697,0.009938916,0.001665525,0.00103354,0.004106109,0.0003862375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03047776,0.004151921,0.5513408,0.003736802,0.00199706,0.0004675865,0.004695558,0.0009483424,0.008583831,0.0003731762,0.002236076,0.390991],"study_design_scores_gemma":[0.0007089688,0.0288631,0.9237174,0.0007874888,0.002574475,0.002614078,0.004420002,0.009219273,0.01659342,0.0005785309,0.009720545,0.000202578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903249,0.002408749,0.002894454,0.00008919814,0.00007787869,0.0004704567,0.0005493179,0.00009018041,0.003094822],"genre_scores_gemma":[0.9882257,0.001534357,0.007941116,0.000135789,0.0000744421,0.0003063335,0.0007963529,0.00004283322,0.0009431139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007896888,"threshold_uncertainty_score":0.04176325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3199273263955948,"score_gpt":0.4750503676452661,"score_spread":0.1551230412496714,"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."}}