{"id":"W2543428929","doi":"10.1111/cgf.13039","title":"Physically Based Video Editing","year":2016,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Human Motion and Animation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Research Foundation Singapore","keywords":"Computer science; Generality; Animation; Physics engine; Object (grammar); Motion (physics); Constraint (computer-aided design); Computer graphics (images); Action (physics); Human–computer interaction; Variety (cybernetics); Frame (networking); Video editing; Task (project management); Computer animation; Computer vision; Artificial intelligence; Geometry","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":[0.0004657147,0.0007311177,0.0003895519,0.0004720276,0.0004429775,0.001164711,0.001737437,0.0007809876,0.01551444],"category_scores_gemma":[0.00197653,0.0003194322,0.0005572232,0.0002206276,0.0006690023,0.000797678,0.001590777,0.0008258538,0.001844205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003665857,"about_ca_system_score_gemma":0.0003923415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001164377,"about_ca_topic_score_gemma":0.001260121,"domain_scores_codex":[0.9995812,0.00008586034,0.00002276027,0.00007883196,0.0001981603,0.00003324954],"domain_scores_gemma":[0.9991137,0.0003772468,0.00007192914,0.0002421248,0.0001255073,0.00006943306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004054961,0.000221375,0.0009096864,0.0007284764,0.00009899964,0.001760478,0.0009988955,0.2483106,0.3038951,0.1037086,0.01816534,0.320797],"study_design_scores_gemma":[0.00009025153,0.0001808476,0.0006752457,0.00009501557,0.00002788739,0.0009226539,0.0001201318,0.8084983,0.0668033,0.02287915,0.09963363,0.00007362091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01378815,0.0001784672,0.961026,0.0001525642,0.0002008872,0.0001517097,0.000121428,0.003254645,0.02112625],"genre_scores_gemma":[0.4562583,0.0005283325,0.512606,0.0002073711,0.0001648305,0.0002384185,0.0004173092,0.001093608,0.02848589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01551444,"threshold_uncertainty_score":0.05190092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008463014124490976,"score_gpt":0.1933407021547201,"score_spread":0.1848776880302292,"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."}}