{"id":"W3040846519","doi":"10.1145/3396250","title":"mSIMPAD","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Computing for Healthcare","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Research Grants Council, University Grants Committee","keywords":"Euclidean distance; Computer science; Scalability; Pattern recognition (psychology); Similarity (geometry); Series (stratigraphy); A priori and a posteriori; Simple (philosophy); Ranging; Wearable computer; Inertial measurement unit; Data mining; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001619223,0.0001452084,0.0002181962,0.00006680714,0.0005935479,0.0001051269,0.0008068071,0.00006103096,0.00001292592],"category_scores_gemma":[0.00005988419,0.0001423842,0.0001963481,0.0005544674,0.00001898071,0.0001642701,0.00002393916,0.0002183263,0.00003032792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003953311,"about_ca_system_score_gemma":0.00006662407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005736573,"about_ca_topic_score_gemma":0.00001917443,"domain_scores_codex":[0.998668,0.00004238171,0.0003033639,0.0004495559,0.0001782354,0.0003584572],"domain_scores_gemma":[0.998807,0.0002335436,0.00009612842,0.0005042735,0.0001232864,0.0002357424],"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":[0.00002793649,0.00004701281,0.0001292845,0.0001387964,0.00004883161,0.000003645735,0.002380755,0.01329497,0.00004874074,0.007754748,0.0003570793,0.9757682],"study_design_scores_gemma":[0.0005240657,0.0008281723,0.0003750676,0.00005863546,0.00001955518,0.00001075752,0.0002451317,0.977289,0.0007226773,0.002199584,0.01740271,0.0003246471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003218751,0.00008348912,0.9461527,0.04966607,0.0002495074,0.0002060367,0.00001405576,0.0003432905,0.00006608001],"genre_scores_gemma":[0.8411759,0.000006572629,0.154854,0.003774759,0.0001331969,0.000007602116,0.000004140721,0.00001425457,0.00002960642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9754435,"threshold_uncertainty_score":0.5806258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06767410526982057,"score_gpt":0.298956634895423,"score_spread":0.2312825296256024,"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."}}