{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001678872,0.0003121613,0.000444481,0.000477735,0.00004191439,0.00009729194,0.0001854976,0.0001689468,0.0001024525],"category_scores_gemma":[0.000002480356,0.0003084136,0.0001131769,0.00003969516,0.0000275147,0.00006592319,0.00002945484,0.0003960983,0.00003497834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002252687,"about_ca_system_score_gemma":0.00002732031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000166176,"about_ca_topic_score_gemma":0.0004782598,"domain_scores_codex":[0.9987619,0.00003311129,0.0003475897,0.0002913308,0.0002980762,0.0002679734],"domain_scores_gemma":[0.9994363,0.00002335125,0.0001144483,0.0003352578,0.00003036114,0.00006025577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003599835,0.0003025217,0.0008166489,0.004164179,0.01313338,0.0003804988,0.05467821,0.581978,0.002472655,0.1734102,0.05738729,0.1109164],"study_design_scores_gemma":[0.003728234,0.0004523273,0.007499968,0.004654357,0.002454957,0.000004659304,0.0002596814,0.1567433,0.002051616,0.001203691,0.816507,0.004440146],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.004339896,0.0001116498,0.006531474,0.000004884266,0.0001683226,0.0004007058,0.00003282885,0.0003691409,0.9880411],"genre_scores_gemma":[0.1610983,0.00001328759,0.0001372473,0.0002222922,0.0002505444,0.00008614078,0.0001927312,0.0001133795,0.8378861],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7591197,"threshold_uncertainty_score":0.9999368,"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."}}