{"id":"W3019368313","doi":"10.36227/techrxiv.12101409.v1","title":"Mobile Crowd Sensing for Hypertensive Patient","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Incentive; Random forest; Computer science; Blood pressure; Key (lock); Classifier (UML); Machine learning; Artificial intelligence; Data mining; Medicine; Computer security; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009413555,0.0002747239,0.0003858065,0.00008863562,0.0000673061,0.0003264117,0.001240944,0.0001852914,0.00000447488],"category_scores_gemma":[0.0001451007,0.0002558098,0.0001554156,0.000106141,0.00003411903,0.0001418833,0.003166696,0.0002851577,0.00002592041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006385908,"about_ca_system_score_gemma":0.0001612928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000781633,"about_ca_topic_score_gemma":0.000002960559,"domain_scores_codex":[0.9982135,0.00003459162,0.0003086685,0.0009452074,0.0002228696,0.0002751358],"domain_scores_gemma":[0.9979314,0.0001239274,0.0001841208,0.001376939,0.0002728441,0.0001107579],"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":[0.00001126109,0.00005703567,0.000007980887,0.0001642784,0.00008774651,0.0000864195,0.00229087,0.0000646585,0.002145748,0.01030213,0.3575447,0.6272372],"study_design_scores_gemma":[0.0003734406,0.001486034,0.000023697,0.0004430714,0.00007165588,0.00008356063,0.0002266977,0.4725476,0.185092,0.1293075,0.2085863,0.001758517],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001084621,0.00007617673,0.9914196,0.0008387052,0.0006630311,0.001101715,0.0001020784,0.001643325,0.003070796],"genre_scores_gemma":[0.09067682,0.00001406451,0.9070156,0.001876235,0.00009476586,0.0001253876,0.00009407806,0.00002606177,0.00007698019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6254787,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04071907144763078,"score_gpt":0.2797926986082365,"score_spread":0.2390736271606058,"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."}}