{"id":"W2964333487","doi":"10.2196/12335","title":"Predicting Energy Expenditure During Gradient Walking With a Foot Monitoring Device: Model-Based Approach","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Physical Activity and Health","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Pusan National University Hospital; Pusan National University; National Research Foundation","keywords":"Energy expenditure; Foot (prosody); Physical medicine and rehabilitation; Computer science; mHealth; Energy (signal processing); Simulation; Psychology; Medicine; Psychological intervention; Statistics; 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":[0.0003561808,0.0007284679,0.0006896533,0.0005950922,0.0002555314,0.0006655783,0.0005761684,0.0007659697,0.001086877],"category_scores_gemma":[0.001066604,0.0003538229,0.0008210842,0.0003834906,0.0001522771,0.0003765174,0.0002715103,0.000514287,0.0002751429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005130353,"about_ca_system_score_gemma":0.000614479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02001191,"about_ca_topic_score_gemma":0.01543205,"domain_scores_codex":[0.9998769,0.00003202559,0.000008031756,0.00004638626,0.00001805113,0.00001872659],"domain_scores_gemma":[0.9996674,0.0002285565,0.00002944467,0.0000126882,0.00004767942,0.00001425392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00011046,0.0001572123,0.01224002,0.00006628547,0.00009769908,0.000062869,0.00003907931,0.9655764,0.001514768,0.0001840341,0.0002733592,0.01967782],"study_design_scores_gemma":[0.000004812082,0.00004282207,0.002235998,0.000006062159,0.00001380352,0.00001227759,0.000008406903,0.9973242,0.0001450022,0.0001314573,0.00007036344,0.000004721525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6288303,0.0006525478,0.3656448,0.0002903478,0.00005283295,0.0001654546,0.000802948,0.0008846679,0.002676013],"genre_scores_gemma":[0.9798544,0.0001431803,0.01866771,0.00003311889,0.00001226658,0.0001406868,0.0003590049,0.00001707146,0.0007725231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02001191,"threshold_uncertainty_score":0.03979087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04770893279586805,"score_gpt":0.3389890074116568,"score_spread":0.2912800746157888,"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."}}