{"id":"W2465249022","doi":"10.1002/cav.1726","title":"Anticipatory balance control and dimension reduction","year":2016,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Human Motion and Animation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion (physics); Character (mathematics); Balance (ability); Object (grammar); Dimension (graph theory); Computation; Parameterized complexity; Reduction (mathematics); Task (project management); Control (management); Artificial intelligence; Credence; Human–computer interaction; Algorithm; Machine learning; Mathematics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008586998,0.00008574732,0.00009339226,0.00007198923,0.00006523989,0.00003891726,0.00002282319,0.00003507849,0.00004111677],"category_scores_gemma":[0.000003556376,0.0000665804,0.00001374263,0.00004871143,0.00003486022,0.0002364098,0.00001150229,0.00003835975,0.00003657965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001776989,"about_ca_system_score_gemma":0.000002295106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.30157e-7,"about_ca_topic_score_gemma":5.462679e-7,"domain_scores_codex":[0.9995601,0.00002399612,0.0001347858,0.0001197645,0.00006912278,0.00009223027],"domain_scores_gemma":[0.9998136,0.00002269846,0.0000213457,0.00006080323,0.00002389833,0.00005766206],"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":[0.00004284841,0.00004715571,0.002552919,0.0001015051,0.00004963803,0.000002565295,0.001241923,0.0005646179,0.4432597,0.03208492,0.01257845,0.5074738],"study_design_scores_gemma":[0.003271941,0.0002928725,0.3503307,0.0002968299,0.00002783898,0.00004408785,0.00006320766,0.6321058,0.002953875,0.0006144924,0.009489271,0.0005091125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7255769,0.000103505,0.2731704,0.0002239765,0.0002929723,0.00008804709,0.000003249155,0.0002307194,0.0003102387],"genre_scores_gemma":[0.998977,0.00008889096,0.0005198764,0.00009084358,0.0001517611,0.000003739636,0.000002911014,0.00001013976,0.0001548503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6315412,"threshold_uncertainty_score":0.271507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009459489473859705,"score_gpt":0.2149506847947096,"score_spread":0.2054911953208499,"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."}}