{"id":"W2741426516","doi":"10.1007/978-3-319-30808-1_152-1","title":"3D Dynamic Pose Estimation from Marker-Based Optical Data","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Pose","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":[0.0003934088,0.001270374,0.0008842941,0.001369166,0.0002304595,0.001761463,0.001472942,0.0009550324,0.01102576],"category_scores_gemma":[0.001061585,0.001153266,0.0008929933,0.001907977,0.0004473754,0.001908718,0.001284579,0.001020571,0.01384082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003519764,"about_ca_system_score_gemma":0.000519269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150485,"about_ca_topic_score_gemma":0.002164216,"domain_scores_codex":[0.9995172,0.0000291853,0.00001841733,0.000118028,0.0002976679,0.00001945395],"domain_scores_gemma":[0.9997075,0.00007888187,0.00003097108,0.0000702213,0.0001009818,0.00001145409],"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":[0.00003930566,0.00002052304,0.0001995326,0.0003172982,0.00003547183,0.00006751088,0.00006827425,0.02345681,0.03022419,0.01051703,0.01602725,0.9190268],"study_design_scores_gemma":[0.00001766372,0.0001363605,0.002897521,0.0006631609,0.0001028172,0.002282192,0.0001600119,0.4136952,0.1055587,0.04325268,0.4310288,0.0002049741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000600407,0.002410844,0.9887152,0.00008054003,0.0002057503,0.00001982415,0.0002184667,0.001787712,0.00596108],"genre_scores_gemma":[0.03975482,0.01398575,0.8945791,0.0002411147,0.0003056938,0.00009129226,0.00255891,0.001239691,0.04724366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01102576,"threshold_uncertainty_score":0.03688484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04122739858946903,"score_gpt":0.3218983450215973,"score_spread":0.2806709464321282,"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."}}