{"id":"W2461499101","doi":"","title":"Empathy in virtual worlds: making characters believable with Laban movement analysis","year":2014,"lang":"en","type":"book","venue":"ETC Press eBooks","topic":"Human Motion and Animation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emily Carr University of Art and Design","funders":"","keywords":"Movement (music); Animation; Empathy; Character animation; Character (mathematics); Motion (physics); Motion capture; Computer science; Metaverse; Motion analysis; Virtual reality; Human–computer interaction; Psychology; Computer animation; Artificial intelligence; Computer graphics (images); Art; Aesthetics; Social psychology; Mathematics","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":[0.0004552174,0.0003937503,0.0001449547,0.0003307412,0.000929609,0.002455177,0.0004483671,0.0005902678,0.006937068],"category_scores_gemma":[0.001119091,0.0001241686,0.0002341798,0.0001689088,0.002268837,0.002697303,0.002561078,0.001423665,0.001471104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702945,"about_ca_system_score_gemma":0.0002532525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002061689,"about_ca_topic_score_gemma":0.000375233,"domain_scores_codex":[0.9996071,0.0002449036,0.00000664993,0.00002965673,0.00007868424,0.000032978],"domain_scores_gemma":[0.9997602,0.0001388195,0.00001217998,0.00002953482,0.00002430103,0.00003500215],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001039098,0.0001875154,0.000601992,0.0004988511,0.00002387264,0.0005474071,0.06043812,0.001998385,0.03347095,0.4327125,0.04952862,0.4198879],"study_design_scores_gemma":[0.00002743776,0.0002808303,0.002479346,0.0004108063,0.00002878567,0.002092052,0.02007952,0.008422649,0.01487905,0.1090404,0.8422043,0.00005482407],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.09568094,0.006123413,0.1728931,0.008410296,0.0007305024,0.0001711213,0.00006509481,0.001058101,0.7148675],"genre_scores_gemma":[0.6357102,0.004309847,0.07034881,0.001854038,0.0001799999,0.0003868024,0.0001563513,0.0004341457,0.2866199],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.006937068,"threshold_uncertainty_score":0.02320677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311272083651846,"score_gpt":0.2097304560768648,"score_spread":0.1966177352403464,"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."}}