{"id":"W2898729409","doi":"10.1002/rob.21813","title":"Developing and deploying a tethered robot to map extremely steep terrain","year":2018,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Toronto","funders":"","keywords":"Computer vision; Terrain; Computer science; Point cloud; Artificial intelligence; Robot; Iterative closest point; Mobile robot; Trajectory; Odometry; Lidar; Leverage (statistics); Remote sensing; Geology; 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":[0.0001535253,0.000317405,0.0001926674,0.000255721,0.0002174459,0.0003315946,0.0006243269,0.0003782268,0.001701694],"category_scores_gemma":[0.0003700753,0.000197044,0.0001825601,0.0001563178,0.0002388023,0.0004446757,0.0005520689,0.000325277,0.000535526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001916868,"about_ca_system_score_gemma":0.0005004699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003479814,"about_ca_topic_score_gemma":0.00389067,"domain_scores_codex":[0.9998853,0.00001360213,0.000004264055,0.00002919382,0.000049229,0.00001831326],"domain_scores_gemma":[0.9998453,0.0000278494,0.00002334947,0.00004081726,0.00004143111,0.00002120026],"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":[0.0002190813,0.0002738526,0.005929128,0.0002068475,0.00007238658,0.000792366,0.0004211203,0.1935886,0.5528482,0.00166089,0.001675102,0.2423125],"study_design_scores_gemma":[0.00009955199,0.001785463,0.008578728,0.00002971021,0.00004629341,0.0004823912,0.0002760988,0.8509104,0.1259004,0.0007402565,0.01109371,0.00005702203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6928301,0.00008374342,0.2974862,0.0001025917,0.00005277658,0.0002370901,0.0001600447,0.004267205,0.004780215],"genre_scores_gemma":[0.8048959,0.00005583448,0.1910902,0.00003372809,0.000006060599,0.0001237662,0.0001587991,0.00006399783,0.00357177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003479814,"threshold_uncertainty_score":0.006919086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02383309187552189,"score_gpt":0.2520062129202815,"score_spread":0.2281731210447596,"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."}}