{"id":"W4224031880","doi":"10.3390/s22082967","title":"On Slip Detection for Quadruped Robots","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Robot; Slip (aerodynamics); Slippage; Robot locomotion; Legged robot; Inertial measurement unit; Artificial intelligence; Search and rescue; Computer science; Terrain; Computer vision; Engineering; Inertial frame of reference; Rescue robot; Simulation; Mobile robot; Control engineering; Robot control; Aerospace engineering; Geography","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":[0.0003148697,0.0005333081,0.0005310802,0.0008373074,0.0003368332,0.000460719,0.0004164721,0.0006337986,0.001025503],"category_scores_gemma":[0.001943156,0.0001994996,0.0002657662,0.0005055669,0.0005450521,0.0006160676,0.0005713187,0.0003540239,0.000395883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002483167,"about_ca_system_score_gemma":0.0002557534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001788248,"about_ca_topic_score_gemma":0.0009151528,"domain_scores_codex":[0.99966,0.00006042802,0.00001943337,0.00005538227,0.0001737333,0.00003100466],"domain_scores_gemma":[0.9993544,0.0002856359,0.00009820963,0.00006380587,0.0001701263,0.00002788546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002824339,0.0001049332,0.004379855,0.0004500359,0.00007087041,0.0006550908,0.0003269833,0.2536044,0.08853768,0.007215857,0.003622393,0.6407496],"study_design_scores_gemma":[0.000009252941,0.0002320554,0.003559229,0.0000595622,0.00001421941,0.0003411575,0.00006617223,0.9704388,0.01341223,0.007205549,0.004633144,0.00002857673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07785444,0.002717779,0.9150898,0.0002148699,0.0001696476,0.00007694621,0.00009217374,0.001065663,0.002718645],"genre_scores_gemma":[0.8072206,0.002053519,0.1866683,0.000198042,0.0001919665,0.00009174123,0.0002714905,0.0001304923,0.00317379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001788248,"threshold_uncertainty_score":0.003555715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007566760680092184,"score_gpt":0.1942762904137575,"score_spread":0.1867095297336653,"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."}}