{"id":"W4416754312","doi":"10.21203/rs.3.rs-8040626/v1","title":"Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach","year":2025,"lang":"","type":"preprint","venue":"Research Square","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Queen's University","funders":"","keywords":"Reliability (semiconductor); Kinematics; Matching (statistics); Autoregressive integrated moving average; Time series; Feature (linguistics)","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":[0.002715433,0.00104134,0.001545719,0.0006786214,0.0004975435,0.001205518,0.00182137,0.001799683,0.003497211],"category_scores_gemma":[0.008718452,0.000652314,0.001220727,0.0006517647,0.000648592,0.001820199,0.001155363,0.00220683,0.0004619593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000932664,"about_ca_system_score_gemma":0.00185942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01108271,"about_ca_topic_score_gemma":0.007754572,"domain_scores_codex":[0.9992961,0.0002302352,0.00003355115,0.000184439,0.0001613599,0.00009427296],"domain_scores_gemma":[0.9951909,0.003542031,0.0003490316,0.0002271697,0.0005917118,0.00009915849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008632507,0.00009397273,0.0003912093,0.00007121115,0.00006087487,0.0000386365,0.00004664137,0.9574097,0.0010841,0.01058467,0.0007817402,0.02935101],"study_design_scores_gemma":[0.000001908376,0.00001148974,0.00005327299,0.000002576642,0.000006875393,0.000004060821,0.000002998988,0.9983925,0.0001089362,0.001330951,0.00008210727,0.000002332952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01343491,0.0001323137,0.9843044,0.0003442538,0.00004096286,0.00002560922,0.00006150296,0.0001981748,0.001457899],"genre_scores_gemma":[0.8495591,0.0005087175,0.1401082,0.0002287749,0.0001755025,0.0002861114,0.0003249557,0.0002807712,0.008527865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01108271,"threshold_uncertainty_score":0.02203637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06522520607493032,"score_gpt":0.4064512666236546,"score_spread":0.3412260605487242,"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."}}