{"id":"W7056910953","doi":"","title":"Identification of essential behavioral components in m-Health to optimize citizens engagement with a physical activity app:The Intelligent Physical Exercise Training app","year":2023,"lang":"en","type":"article","venue":"University of Southern Denmark Research Portal (University of Southern Denmark)","topic":"Laser Design and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Physical activity; Identification (biology); Training (meteorology); Physical exercise; Component (thermodynamics)","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":[0.0004293842,0.0007631115,0.0003416621,0.0004326467,0.0002273581,0.0007140886,0.0003231733,0.0005362749,0.002335365],"category_scores_gemma":[0.002008614,0.0002427167,0.0003498695,0.0001902647,0.0001376435,0.00051285,0.0005943071,0.000399877,0.0008241177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282644,"about_ca_system_score_gemma":0.0006188726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001431313,"about_ca_topic_score_gemma":0.003112022,"domain_scores_codex":[0.9997153,0.00005811855,0.00001935118,0.00007173473,0.00009199246,0.00004357983],"domain_scores_gemma":[0.9994748,0.0002374267,0.0000659197,0.00003123954,0.0001399593,0.00005067961],"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":[0.004567832,0.004250215,0.1359216,0.002602926,0.0002641817,0.0004217423,0.001990641,0.004265267,0.1237273,0.001991536,0.008246072,0.7117507],"study_design_scores_gemma":[0.0003414848,0.006213285,0.7439877,0.001287956,0.001055225,0.001088964,0.002591681,0.1111459,0.1066747,0.007234981,0.01814946,0.0002285953],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8901867,0.000792957,0.09075098,0.0005577764,0.0001439944,0.001414254,0.001579598,0.002563773,0.01201001],"genre_scores_gemma":[0.8871896,0.0004605938,0.1037922,0.0003186515,0.0000290748,0.001122365,0.001109694,0.0001560576,0.005821727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002335365,"threshold_uncertainty_score":0.007812619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06920917401925358,"score_gpt":0.2906645831055836,"score_spread":0.22145540908633,"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."}}