{"id":"W2556232943","doi":"10.2196/mhealth.6953","title":"Correction of: Sleep Quality Prediction From Wearable Data Using Deep Learning","year":2016,"lang":"en","type":"erratum","venue":"JMIR mhealth and uhealth","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Computer science; Sleep quality; Wearable technology; Artificial intelligence; Deep learning; Data quality; Quality (philosophy); Machine learning; Data science; Psychology; Engineering","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":[0.006451405,0.002447912,0.002607335,0.003618648,0.002844642,0.004219603,0.0042974,0.007783493,0.05742085],"category_scores_gemma":[0.1337323,0.001292645,0.002476009,0.002352265,0.003264585,0.002531518,0.002992984,0.01155135,0.03525921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003885988,"about_ca_system_score_gemma":0.006651238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01257294,"about_ca_topic_score_gemma":0.01598804,"domain_scores_codex":[0.9912806,0.001263475,0.002220342,0.001063889,0.003613274,0.0005584073],"domain_scores_gemma":[0.9317579,0.01632381,0.003902598,0.003892091,0.04202609,0.002097586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006254051,0.000006451201,0.0001277307,0.000175242,0.00002125775,0.0004785709,0.00004268501,0.00006096924,0.00005593572,0.0005813653,0.9918851,0.00650218],"study_design_scores_gemma":[0.0001352358,0.00005855519,0.0009761134,0.001138582,0.00009379401,0.00257786,0.0002029884,0.0009857545,0.0008324987,0.003794054,0.9891076,0.00009695537],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001944795,0.001026197,0.001534302,0.06921751,0.9240882,0.00003876739,0.00184098,0.0007448806,0.001314557],"genre_scores_gemma":[0.02803613,0.01120727,0.01454458,0.2160148,0.5510735,0.000584577,0.006437894,0.004223983,0.1678773],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05742085,"threshold_uncertainty_score":0.1920919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0808038199535013,"score_gpt":0.3859574811423992,"score_spread":0.3051536611888979,"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."}}