{"id":"W2998451125","doi":"10.1080/10255842.2019.1705798","title":"Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data","year":2019,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"","keywords":"Kinematics; Joint (building); Scaling; Orientation (vector space); Parametric statistics; Computer science; Multidimensional scaling; Consistency (knowledge bases); Statistical parametric mapping; Parametric model; Artificial intelligence; Computer vision; Algorithm; Geometry; Mathematics; Physics; Statistics; Engineering; Structural engineering; Machine learning","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":[0.004462705,0.0009566015,0.0006394302,0.001936076,0.0002282406,0.00141416,0.0007534071,0.0006987842,0.001665245],"category_scores_gemma":[0.01976039,0.000430646,0.000779375,0.001337893,0.0007271192,0.001002118,0.001119661,0.0003377498,0.000841835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002469629,"about_ca_system_score_gemma":0.0003479425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001625129,"about_ca_topic_score_gemma":0.00136766,"domain_scores_codex":[0.9963899,0.001478429,0.0003714028,0.0007633304,0.0008730527,0.0001238812],"domain_scores_gemma":[0.9903463,0.004861616,0.0009205076,0.002307269,0.001462379,0.0001019847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005172846,0.0006313607,0.08446395,0.001813634,0.00120709,0.0004539275,0.0031298,0.1318508,0.1068043,0.00148385,0.001217458,0.6617709],"study_design_scores_gemma":[0.0001500719,0.004743813,0.3079221,0.0003677573,0.0006948047,0.001509903,0.002166137,0.5958556,0.07807316,0.002376217,0.005857903,0.000282461],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8126995,0.001095134,0.1825546,0.00007858404,0.00008119897,0.0001837107,0.000518099,0.0007985128,0.001990674],"genre_scores_gemma":[0.9606523,0.0003168705,0.03760574,0.00001332294,0.00001755005,0.00005159822,0.0005948552,0.0002073729,0.0005404209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004462705,"threshold_uncertainty_score":0.02360129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08113962257014092,"score_gpt":0.3664052528857076,"score_spread":0.2852656303155667,"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."}}