{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003989591,0.0004958641,0.0004172303,0.0003242129,0.0002472854,0.0004340982,0.0005796893,0.0003121491,0.001899441],"category_scores_gemma":[0.001487275,0.0003029772,0.0003760875,0.0002155113,0.0004703157,0.000633615,0.0007899635,0.0006198561,0.0001864694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003146251,"about_ca_system_score_gemma":0.0003305326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002852222,"about_ca_topic_score_gemma":0.002185574,"domain_scores_codex":[0.9998195,0.00004755221,0.00001099274,0.00005005353,0.00004950036,0.00002257002],"domain_scores_gemma":[0.9995735,0.0002004189,0.00006796971,0.00007198942,0.00006580529,0.00002041919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001733182,0.00006313998,0.0006348829,0.0000567147,0.00003167398,0.00004403104,0.0001045269,0.8363541,0.01657093,0.02307981,0.000717892,0.1221689],"study_design_scores_gemma":[0.000007166743,0.00003668598,0.0001247149,0.000002915193,0.000003728593,0.000006991205,0.000004921949,0.9927477,0.001270482,0.00544957,0.000340774,0.000004396692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05509946,0.0001283269,0.9416674,0.0001477463,0.00002974423,0.00003247472,0.00003817349,0.0004357199,0.002420956],"genre_scores_gemma":[0.8913463,0.00006845772,0.1067776,0.00004806489,0.00002142411,0.00007411892,0.00006555361,0.00004787604,0.001550504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002852222,"threshold_uncertainty_score":0.006354272,"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."}}