{"id":"W2076806230","doi":"10.1145/2366145.2366173","title":"Terrain runner","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministry of Education - Singapore","keywords":"Computer science; Terrain; Continuation; Obstacle; Process (computing); Control theory (sociology); Motion (physics); Trajectory; Simulation; Artificial intelligence; Computer vision; Control (management); Law","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":[],"consensus_categories":[],"category_scores_codex":[0.00007295806,0.0000771129,0.00005305812,0.0001340412,0.00008617234,0.00001361,0.00007632935,0.00005860507,0.0004270207],"category_scores_gemma":[0.000004142972,0.00007761747,0.00005726474,0.0001677055,0.00001743779,0.0001699388,5.759303e-7,0.0001594517,0.0002195476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001601879,"about_ca_system_score_gemma":0.000001940789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001749431,"about_ca_topic_score_gemma":0.00001066743,"domain_scores_codex":[0.9996036,0.00001084237,0.00009158409,0.0000524129,0.0000864725,0.0001550553],"domain_scores_gemma":[0.9996952,0.00002124655,0.000006852802,0.0001961367,0.00001036015,0.00007016087],"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":[0.00006851323,0.002608894,0.005620729,0.0006144418,0.0009367497,0.000008449295,0.02613595,0.07392328,0.05464702,0.09576105,0.0366728,0.7030022],"study_design_scores_gemma":[0.003434623,0.0003852824,0.1590954,0.0002510828,0.0003223602,0.00008446881,0.001678207,0.05610672,0.04412486,0.01954704,0.7119066,0.003063302],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2219621,0.0001435938,0.7662687,0.000561,0.001241,0.0001603335,0.00002144947,0.001126677,0.00851519],"genre_scores_gemma":[0.9985259,0.00006366701,0.0009508608,0.0002003991,0.00006114123,0.00001277883,0.000005085886,0.00001914567,0.0001609742],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7765639,"threshold_uncertainty_score":0.4675578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210172069993978,"score_gpt":0.2350150555078467,"score_spread":0.2139978485084489,"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."}}