{"id":"W4405005005","doi":"10.3389/frobt.2024.1384575","title":"L-AVATeD: The lidar and visual walking terrain dataset","year":2024,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Stairs; Computer science; Gait; Exoskeleton; Terrain; Climbing; Distraction; Human–computer interaction; Physical medicine and rehabilitation; Simulation; Artificial intelligence; Medicine; Psychology; Engineering; Cognitive psychology","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":[0.0005649512,0.002713972,0.001759957,0.002432507,0.0008693409,0.001514775,0.003814499,0.002347204,0.01048257],"category_scores_gemma":[0.002160048,0.000516578,0.001635617,0.002303852,0.0004840585,0.001117478,0.002428473,0.001940915,0.02151598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100009,"about_ca_system_score_gemma":0.001212163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02995201,"about_ca_topic_score_gemma":0.08217718,"domain_scores_codex":[0.99911,0.000116925,0.00008432587,0.0002536353,0.0002904276,0.0001446357],"domain_scores_gemma":[0.9995746,0.00005136254,0.00002953254,0.0001394489,0.0001504428,0.00005448214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003770818,0.000292008,0.006731179,0.001348092,0.0002735875,0.0004273472,0.0001229713,0.00281673,0.001853936,0.000751101,0.950191,0.03481488],"study_design_scores_gemma":[0.0007210273,0.0002561399,0.04712016,0.001003827,0.0002319135,0.001525547,0.001003489,0.02603195,0.00368676,0.005191223,0.912919,0.0003089699],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008027103,0.001434322,0.002335652,0.0005535149,0.0003088072,0.0001590604,0.9774529,0.006064462,0.003664179],"genre_scores_gemma":[0.007663825,0.0001999559,0.002402435,0.000114494,0.00002660148,0.0001450681,0.9886263,0.0001269266,0.0006943831],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02995201,"threshold_uncertainty_score":0.05955535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004409361520941611,"score_gpt":0.2300548104576773,"score_spread":0.2256454489367357,"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."}}