{"id":"W4360998034","doi":"10.1371/journal.pdig.0000211","title":"Capturing children food exposure using wearable cameras and deep learning","year":2023,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Wearable computer; Artificial intelligence; Deep learning; Computer science; Influencer marketing; Machine learning; Recall; Psychology","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":[0.000188233,0.0004511702,0.0002465011,0.000533572,0.0001248013,0.0003500663,0.0004117968,0.0003561,0.0008779273],"category_scores_gemma":[0.0006747179,0.0002099079,0.000297065,0.0004705319,0.0001520714,0.0004144704,0.0004160814,0.0004009408,0.000296459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004393757,"about_ca_system_score_gemma":0.0002218648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006641692,"about_ca_topic_score_gemma":0.01501916,"domain_scores_codex":[0.9997861,0.00003418211,0.00001005929,0.00007789891,0.00005925445,0.00003257443],"domain_scores_gemma":[0.9998022,0.00005535973,0.0000414764,0.0000192263,0.00006756733,0.00001426513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003007882,0.0006033714,0.114036,0.0004768495,0.0002297804,0.000546265,0.0008648928,0.03296521,0.1561407,0.001230614,0.005363905,0.6872416],"study_design_scores_gemma":[0.00004316464,0.0006804952,0.2765025,0.0001637465,0.000124096,0.0008011842,0.001069296,0.5749721,0.1319637,0.001768483,0.01181845,0.00009276476],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7691815,0.0006907659,0.2209592,0.0003118083,0.00008308494,0.0002438251,0.002044039,0.001451378,0.005034378],"genre_scores_gemma":[0.8121735,0.0005517309,0.1824263,0.0001911228,0.00003049501,0.0002519473,0.001284015,0.0000473962,0.00304351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006641692,"threshold_uncertainty_score":0.01320606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03481164663727945,"score_gpt":0.2756033655085136,"score_spread":0.2407917188712341,"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."}}