{"id":"W2791785921","doi":"10.5220/0006624205110521","title":"Soft-tissue Artefact Assessment and Compensation in Motion Analysis by Combining Motion Capture Data and Ultrasound Depth Measurements","year":2018,"lang":"en","type":"article","venue":"","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Motion compensation; Compensation (psychology); Motion (physics); Motion capture; Computer science; Motion analysis; Computer vision","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":[0.001001865,0.0001743916,0.0003663719,0.000383272,0.00009798261,0.00004010365,0.00009022954,0.0001134974,0.000187724],"category_scores_gemma":[0.0001205494,0.0001479411,0.00002134787,0.0005985288,0.0001387193,0.0002449237,0.00004786058,0.0002002338,0.000002574561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008391203,"about_ca_system_score_gemma":0.00002369702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004534549,"about_ca_topic_score_gemma":0.0007242248,"domain_scores_codex":[0.9984003,0.0001216408,0.0003397722,0.0004486174,0.0004805347,0.0002090972],"domain_scores_gemma":[0.9990415,0.00008386801,0.000121402,0.0004874993,0.0001439712,0.0001217393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004333835,0.0001809127,0.950784,0.00003979178,0.0003884036,0.000004070021,0.0007010533,0.000005672945,0.03533552,0.00004056019,0.0006105572,0.0118661],"study_design_scores_gemma":[0.002385561,0.0002864886,0.981745,0.00008262809,0.0009334577,0.00002904882,0.0009231037,0.01162426,0.00158687,0.0001195463,0.0001262279,0.0001578232],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9151731,0.0002264141,0.0809745,0.0004718475,0.00009628788,0.0004696715,0.00002706694,0.00005416021,0.002507004],"genre_scores_gemma":[0.9947109,0.00004704747,0.003436561,0.0003440692,0.00006867758,0.000006566881,0.001323745,0.00001493715,0.00004748959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07953785,"threshold_uncertainty_score":0.6032863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0506726959442475,"score_gpt":0.3425392090874451,"score_spread":0.2918665131431976,"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."}}