{"id":"W4404966485","doi":"10.1145/3680528.3687702","title":"Measuring Human Motion Under Clothing","year":2024,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Clothing; Human motion; Computer science; Motion (physics); Computer vision; Geography","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":[0.0001503274,0.0005013697,0.0003627507,0.0008639441,0.0002505795,0.0004762305,0.0002379765,0.0004841949,0.002361125],"category_scores_gemma":[0.0007349181,0.000315301,0.0002864717,0.0006024526,0.000174728,0.0003368197,0.0004201002,0.0001691307,0.000866513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000119559,"about_ca_system_score_gemma":0.0001560517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001741489,"about_ca_topic_score_gemma":0.002731115,"domain_scores_codex":[0.9996798,0.0000608099,0.0000109347,0.00009663748,0.0001078997,0.00004392049],"domain_scores_gemma":[0.9997627,0.00006223256,0.00002408274,0.00004010586,0.00006426292,0.00004666131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009497025,0.0001590799,0.03205527,0.0002980706,0.0002037968,0.0004907458,0.0005229367,0.01760858,0.7179757,0.0004739919,0.001798824,0.2274632],"study_design_scores_gemma":[0.00005886358,0.001259389,0.4912884,0.00007895048,0.0002531394,0.004420545,0.001060334,0.3042116,0.1887722,0.0007850244,0.007709219,0.0001022167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8811522,0.0003131436,0.1121743,0.00004148841,0.00008032933,0.00005521212,0.0006006148,0.0005535661,0.005028975],"genre_scores_gemma":[0.9801987,0.0001858312,0.01705184,0.00002549759,0.00001617788,0.000014958,0.0003826977,0.00006702885,0.002057349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002361125,"threshold_uncertainty_score":0.007898748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04493517135502752,"score_gpt":0.2350732209359359,"score_spread":0.1901380495809084,"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."}}