{"id":"W2151621026","doi":"10.1142/s0219843612500028","title":"HUMANOID FALL AVOIDANCE USING A MIXTURE OF STRATEGIES","year":2012,"lang":"en","type":"article","venue":"International Journal of Humanoid Robotics","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Humanoid robot; Computer science; Robot; Simulation; Overhead (engineering); Ankle; Falling (accident); Artificial intelligence","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":[0.0004307905,0.0006446792,0.0004539036,0.0004894529,0.0003452384,0.0007912713,0.0005036931,0.00041317,0.001209632],"category_scores_gemma":[0.001370957,0.0002396645,0.000237791,0.0001720219,0.0003942581,0.0004091244,0.0008358413,0.0003217591,0.0003312982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002287283,"about_ca_system_score_gemma":0.0004961246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00257573,"about_ca_topic_score_gemma":0.002148476,"domain_scores_codex":[0.9996653,0.00006359386,0.00002381815,0.00008173514,0.0001227035,0.000042798],"domain_scores_gemma":[0.9996397,0.0001084025,0.00007217706,0.00004243253,0.00008997267,0.00004725441],"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":[0.001062687,0.0005275357,0.00932865,0.0004421501,0.0002207082,0.0006120956,0.001506147,0.1353994,0.1985836,0.007873729,0.001786188,0.6426572],"study_design_scores_gemma":[0.0002178778,0.001596336,0.009486617,0.0001401689,0.0001493826,0.0009836121,0.0004637519,0.9398329,0.0297299,0.009828747,0.007461004,0.0001097094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2390151,0.0004695372,0.7473202,0.0001815014,0.00004182509,0.000313023,0.00003201942,0.002045713,0.01058119],"genre_scores_gemma":[0.9273618,0.0001181537,0.06974078,0.00007795216,0.000009269826,0.0001495732,0.00002319742,0.00002910313,0.002490118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00257573,"threshold_uncertainty_score":0.005121529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506253226738008,"score_gpt":0.269295139290844,"score_spread":0.254232607023464,"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."}}