{"id":"W2811079226","doi":"10.1145/3197517.3201296","title":"The human touch","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Human body; Finite element method; Pipeline (software); Population; Animation; Body shape; Clothing; Human–computer interaction; Artificial intelligence; Computer graphics (images); Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003454675,0.0006360291,0.000484184,0.0005705313,0.0009990005,0.002838543,0.0009378229,0.001895033,0.0216446],"category_scores_gemma":[0.00230485,0.0004692142,0.0006718542,0.0003972483,0.002228595,0.003415445,0.003050863,0.001180038,0.004806093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004204974,"about_ca_system_score_gemma":0.000536442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009706476,"about_ca_topic_score_gemma":0.0005534743,"domain_scores_codex":[0.9993998,0.0001127054,0.00002212902,0.0001914627,0.0002240365,0.00004987916],"domain_scores_gemma":[0.9995173,0.0001763887,0.00003058156,0.0001435818,0.00006791853,0.00006413036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005573133,0.0001476454,0.005088592,0.00163079,0.0001629025,0.00175365,0.003168239,0.03121772,0.1337068,0.3658953,0.05981958,0.3968515],"study_design_scores_gemma":[0.0001133594,0.0007226581,0.01475272,0.001007499,0.0001468007,0.007547615,0.002282391,0.148831,0.030527,0.2775833,0.51616,0.0003256203],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05873105,0.01212935,0.6179503,0.006400169,0.002702042,0.000356842,0.001522292,0.002853537,0.2973543],"genre_scores_gemma":[0.8175653,0.006586867,0.08609681,0.002145373,0.0004600043,0.000321268,0.0006406139,0.000427245,0.08575653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0216446,"threshold_uncertainty_score":0.07240838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01978056475333809,"score_gpt":0.2499661699069711,"score_spread":0.230185605153633,"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."}}