{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003772322,0.0005992254,0.0007565586,0.0006677687,0.0003786841,0.0005189911,0.0005206014,0.0006115964,0.001286624],"category_scores_gemma":[0.0009741241,0.0001675024,0.0003402473,0.0005448872,0.0001704079,0.0003997443,0.0008010081,0.0004482329,0.0004018856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002809804,"about_ca_system_score_gemma":0.0003733882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003191943,"about_ca_topic_score_gemma":0.002701389,"domain_scores_codex":[0.9996468,0.000103227,0.00001536002,0.0000881265,0.00008320416,0.00006329839],"domain_scores_gemma":[0.9996545,0.0001521221,0.00004285631,0.00002603316,0.00006847962,0.00005591169],"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":[0.003586122,0.00127704,0.06929429,0.001152251,0.0003637694,0.004376931,0.001345859,0.1617364,0.08146509,0.007805837,0.05011642,0.61748],"study_design_scores_gemma":[0.00006141837,0.0004033306,0.01414628,0.00005542161,0.00005915476,0.0007326702,0.0006900732,0.9653846,0.006427573,0.005008974,0.006986976,0.00004353262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5326767,0.005989815,0.4301259,0.003985455,0.001517717,0.0006425125,0.003667589,0.00261785,0.01877642],"genre_scores_gemma":[0.9611251,0.000755218,0.03365294,0.0002695482,0.0003286337,0.0001186971,0.0007659636,0.00002412426,0.002959722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003191943,"threshold_uncertainty_score":0.006346762,"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."}}