{"id":"W1963565453","doi":"10.1109/tmech.2011.2159388","title":"3-D Active Sensing in Time-Critical Urban Search and Rescue Missions","year":2011,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Urban search and rescue; Search and rescue; Rescue robot; Computer science; Robustness (evolution); Rubble; Artificial intelligence; Computer vision; Robot; Mobile robot; Real-time computing; 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":[0.0001422471,0.0001759768,0.0001797555,0.0002394458,0.0001343777,0.0000320651,0.000080924,0.0001699766,0.0001217633],"category_scores_gemma":[0.00001041454,0.0001888471,0.00005544743,0.0002477501,0.00005724742,0.0001321294,0.000002062373,0.0004730123,0.00005563271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443856,"about_ca_system_score_gemma":0.0000384277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008720172,"about_ca_topic_score_gemma":0.0001069406,"domain_scores_codex":[0.9989223,0.00006808391,0.000219006,0.0002348601,0.0001984998,0.0003571813],"domain_scores_gemma":[0.9994819,0.00009013876,0.000005936851,0.0002202777,0.00004493696,0.000156851],"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":[0.0001151459,0.0002205848,0.00001673086,0.00008301475,0.00006336851,0.00003345737,0.002638389,0.9600049,0.01661883,0.002081399,0.0000776985,0.01804648],"study_design_scores_gemma":[0.0004268963,0.0001081289,0.00008920288,0.00008711821,0.00003426158,0.00001335423,0.0002514827,0.9454232,0.05282383,0.000339364,0.0001445101,0.0002586393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.210739,0.00008071638,0.7876294,0.0001276421,0.0003111122,0.0002270574,0.00003317002,0.0002233665,0.0006284968],"genre_scores_gemma":[0.9915273,0.000122211,0.008117764,0.00003268425,0.00002667796,0.000004250984,0.000005458429,0.00005174771,0.000111922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7807882,"threshold_uncertainty_score":0.7700962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220782816541516,"score_gpt":0.2331683191739186,"score_spread":0.2109604910085034,"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."}}