{"id":"W4295758158","doi":"10.2196/33606","title":"Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Physical Activity and Health","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Artificial intelligence; Deep learning; Computer science; Mean squared error; Machine learning; Set (abstract data type); Energy (signal processing); Estimation; Statistics; Engineering; Mathematics","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":[0.0002631899,0.0008031264,0.0005508074,0.0004912899,0.0001757315,0.0004552211,0.000901209,0.000660579,0.001607638],"category_scores_gemma":[0.0009116338,0.0003293746,0.0005099903,0.0005007855,0.000205711,0.0005625595,0.0005556067,0.000898735,0.000474595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006121391,"about_ca_system_score_gemma":0.0004934404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103294,"about_ca_topic_score_gemma":0.01228342,"domain_scores_codex":[0.9998118,0.00002262983,0.000007557428,0.00008203193,0.00004053914,0.00003551405],"domain_scores_gemma":[0.9998279,0.00006283353,0.00002444633,0.00002189001,0.00004835662,0.00001455341],"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":[0.0002326028,0.0003265759,0.004102789,0.0001043534,0.0001178104,0.0001491573,0.00008617822,0.4729351,0.0144941,0.00147496,0.005352102,0.5006242],"study_design_scores_gemma":[0.00000523089,0.00002524963,0.0008981171,0.000006956534,0.000008478209,0.0000227244,0.000008119861,0.995499,0.001874743,0.001193541,0.000451514,0.000006330404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09157688,0.001104723,0.8986633,0.0005151028,0.0001302407,0.00006352368,0.0004975381,0.002898281,0.004550504],"genre_scores_gemma":[0.890169,0.0003115,0.1035252,0.0003403428,0.00006402126,0.00007545669,0.0006892199,0.0001041847,0.004721091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0103294,"threshold_uncertainty_score":0.02053857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07071962440691125,"score_gpt":0.4235913542791971,"score_spread":0.3528717298722859,"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."}}