{"id":"W2947737216","doi":"10.5210/ojphi.v11i1.9795","title":"Improving Public Health Surveillance methods via Smart Home technologies","year":2019,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Data science; Public health surveillance; Public health; Medicine; Data mining; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.03875669,0.0004556733,0.001784334,0.001406624,0.001376479,0.00007817132,0.001236162,0.0005474177,0.0002167793],"category_scores_gemma":[0.003534252,0.0003757657,0.0002279198,0.001850593,0.0001436069,0.001354659,0.0003864517,0.003609141,0.0002976692],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002060521,"about_ca_system_score_gemma":0.02386965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000289988,"about_ca_topic_score_gemma":0.0002481441,"domain_scores_codex":[0.9840826,0.002617375,0.00869344,0.0002940827,0.001064709,0.003247749],"domain_scores_gemma":[0.9820907,0.001679213,0.01070383,0.001210036,0.001989314,0.002326877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001690158,0.0002836325,0.02922827,0.004940839,0.00004030668,6.671363e-7,0.002608194,0.00000844014,0.000007084383,0.003689502,0.01134308,0.9478331],"study_design_scores_gemma":[0.00206418,0.0009735689,0.01162981,0.0002990903,0.000004262551,0.00009077619,0.01527838,0.005223939,0.000001129544,0.0005130017,0.9636198,0.0003020867],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03500085,0.006216658,0.624523,0.319573,0.005384584,0.006835931,0.0003024795,0.0007642633,0.00139924],"genre_scores_gemma":[0.1156534,0.02152876,0.7895353,0.06948356,0.00135045,0.0008517864,0.0004571306,0.0002036956,0.0009359056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9522767,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128662379308611,"score_gpt":0.4724160353238973,"score_spread":0.3595497973930362,"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."}}