{"id":"W3206434649","doi":"10.55417/fr.2022055","title":"System for Multi-Robotic Exploration of Underground Environments CTU-CRAS-NORLAB in the DARPA Subterranean Challenge","year":2022,"lang":"en","type":"article","venue":"Field Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"CONTEST; Agency (philosophy); Field (mathematics); Global Positioning System; Robot; Computer science; Computer security; Engineering; Aeronautics; Systems engineering; Engineering management; Telecommunications; Artificial intelligence; Political science","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":[0.001263358,0.0007639614,0.0005673728,0.0005203218,0.0007587972,0.0009192089,0.00116237,0.0007138167,0.008858103],"category_scores_gemma":[0.001239238,0.000224581,0.0002733503,0.0003382799,0.0005405938,0.0009737325,0.002720629,0.001030757,0.005108981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006700646,"about_ca_system_score_gemma":0.001939945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008029673,"about_ca_topic_score_gemma":0.008619515,"domain_scores_codex":[0.9994541,0.0001184666,0.00001961435,0.0001059158,0.0001766347,0.0001251859],"domain_scores_gemma":[0.9991385,0.00007794308,0.00003751073,0.0001805693,0.0003142796,0.0002510947],"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":[0.003598816,0.000759809,0.01025757,0.00060391,0.0002463635,0.001221339,0.001419831,0.05370698,0.1287111,0.03428148,0.3280929,0.4370999],"study_design_scores_gemma":[0.000802945,0.002078558,0.006847518,0.0001264668,0.00009509947,0.0006873794,0.000668193,0.5735477,0.04353707,0.007976585,0.3634956,0.0001369294],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1297818,0.0009348604,0.7086043,0.002604836,0.001135955,0.001609156,0.003379412,0.07563633,0.07631335],"genre_scores_gemma":[0.5905699,0.0003240488,0.35517,0.0006600592,0.0001462569,0.001480269,0.008277047,0.001474773,0.04189778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008858103,"threshold_uncertainty_score":0.02963334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06802371110501453,"score_gpt":0.2481615626143525,"score_spread":0.180137851509338,"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."}}