{"id":"W2129287493","doi":"10.1023/a:1012426720699","title":"RHex: A Biologically Inspired Hexapod Runner","year":2001,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":410,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Hexapod; Inverted pendulum; Odometry; Terrain; Robot; Visual odometry; Computer vision; Gait; Visual servoing; Obstacle avoidance; Rendering (computer graphics); Artificial intelligence; Simulation; Control theory (sociology); Mobile robot; Physics; Physical medicine and rehabilitation","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":[0.00006924976,0.0003639853,0.0003034515,0.0001451755,0.0002733156,0.0002670715,0.0007245647,0.0004970263,0.004740657],"category_scores_gemma":[0.00008088545,0.0001780306,0.0002356972,0.0000655161,0.0003896678,0.0003650137,0.0006126013,0.0004380804,0.001327992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092209,"about_ca_system_score_gemma":0.0001628177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003715301,"about_ca_topic_score_gemma":0.0005936437,"domain_scores_codex":[0.9999661,0.000003417684,0.00000142582,0.00001269688,0.00001101835,0.000005359453],"domain_scores_gemma":[0.9999703,0.000004079164,0.000003357909,0.000006224082,0.000003573215,0.00001245427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003608184,0.0001343298,0.001043839,0.0003574091,0.00006420125,0.00056395,0.0001573425,0.04445326,0.8107043,0.01327015,0.004325533,0.1245649],"study_design_scores_gemma":[0.0003390068,0.002637204,0.006692635,0.0001755264,0.000156756,0.00240226,0.0003282406,0.4083498,0.4303285,0.0185695,0.1298334,0.0001871621],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4189677,0.001391999,0.5226342,0.0004874739,0.0004703593,0.0003872666,0.0007008677,0.01872973,0.03623042],"genre_scores_gemma":[0.659046,0.0006708754,0.2918629,0.0002815741,0.00003005052,0.0002454205,0.0008027318,0.000795858,0.04626461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004740657,"threshold_uncertainty_score":0.01585907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01154586250401591,"score_gpt":0.2006926071955425,"score_spread":0.1891467446915266,"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."}}