{"id":"W2475210682","doi":"10.1145/2897824.2925893","title":"Task-based locomotion","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Retargeting; Animation; Task (project management); Motion (physics); Motion capture; Coarticulation; Artificial intelligence; Computer vision; Character animation; Human–computer interaction; Computer animation; Computer graphics (images); Speech recognition; 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":[0.0001485126,0.0004898527,0.0002546442,0.0001693847,0.0002889635,0.0006634219,0.0007127204,0.000704822,0.006467971],"category_scores_gemma":[0.0007966324,0.0002191819,0.0005082786,0.0001472627,0.0004194517,0.0005689253,0.0009583036,0.0004703312,0.001938839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858897,"about_ca_system_score_gemma":0.0004590525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605758,"about_ca_topic_score_gemma":0.002046757,"domain_scores_codex":[0.9998605,0.00002754747,0.000007263536,0.00003923822,0.000045245,0.00002009804],"domain_scores_gemma":[0.9998032,0.00003761215,0.00002063102,0.00006640374,0.00003969871,0.00003242587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000194035,0.0001605385,0.00204609,0.0004113292,0.00005605786,0.0004160949,0.0004416516,0.6582549,0.1124171,0.09473322,0.01078928,0.1200797],"study_design_scores_gemma":[0.00002262486,0.0001408136,0.001601962,0.0000357675,0.00001916312,0.0002682066,0.00006706215,0.941601,0.006795453,0.02133468,0.02808208,0.0000311896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05238714,0.0003200567,0.8949693,0.0002815999,0.0001319657,0.0002064359,0.0005378845,0.002182111,0.04898348],"genre_scores_gemma":[0.7844659,0.0005433999,0.1835393,0.0001456032,0.00003786424,0.0004377054,0.0009712404,0.0005520225,0.02930693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006467971,"threshold_uncertainty_score":0.0216375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657249408395428,"score_gpt":0.2167920848303812,"score_spread":0.200219590746427,"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."}}