{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001672549,0.0001136926,0.0001882249,0.0001370644,0.00006426239,0.00006043915,0.0001095775,0.00008878047,0.00001862932],"category_scores_gemma":[0.00009621547,0.0001031947,0.00003947398,0.0001246896,0.00001595623,0.00009133767,0.00002475644,0.0001647872,0.000006982851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004910569,"about_ca_system_score_gemma":0.0000282894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004403393,"about_ca_topic_score_gemma":0.00003152666,"domain_scores_codex":[0.9992111,0.00001974549,0.0003576047,0.00007618876,0.0001534302,0.0001819847],"domain_scores_gemma":[0.9994816,0.00008190029,0.00007044794,0.0001018824,0.0001503705,0.0001137892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003570873,0.00001713388,0.001378905,0.0001054439,0.00009210211,0.0000470521,0.001422301,0.9610679,0.01041544,0.001191857,0.003614206,0.02061198],"study_design_scores_gemma":[0.002441299,0.001952883,0.005040266,0.001773007,0.000222075,0.0004803394,0.001542764,0.9098428,0.05191111,0.003632936,0.01966838,0.001492147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02957584,0.0002273853,0.9671119,0.001965784,0.0007422054,0.00006701609,3.553577e-7,0.00003240378,0.0002771362],"genre_scores_gemma":[0.7962199,0.00007070598,0.202483,0.0006707208,0.0004619655,4.132904e-7,3.809027e-7,0.00003019995,0.00006267463],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7666441,"threshold_uncertainty_score":0.4208156,"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."}}