{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003423922,0.0002979729,0.0003168206,0.0004238051,0.000250868,0.0006407751,0.0005290653,0.0006345656,0.0006346261],"category_scores_gemma":[0.000927955,0.0002684114,0.0002122714,0.0005396463,0.0006402236,0.000729079,0.0006637833,0.0003649208,0.0002042191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000266276,"about_ca_system_score_gemma":0.0002472581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001543966,"about_ca_topic_score_gemma":0.001982908,"domain_scores_codex":[0.9997936,0.00007063311,0.000007559164,0.00002400461,0.00008980673,0.0000143898],"domain_scores_gemma":[0.9996846,0.0001815907,0.00003316532,0.00002787561,0.00005299561,0.00001984442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007445126,0.0001053477,0.003250415,0.0004776063,0.00005783251,0.001069479,0.0009313276,0.5169895,0.1620524,0.02535678,0.004076361,0.2848884],"study_design_scores_gemma":[0.00002973783,0.0001797996,0.002914613,0.00005608134,0.00003137941,0.0005760536,0.0003699053,0.9471994,0.02450646,0.01533285,0.008750709,0.00005313823],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08355839,0.002543512,0.9075049,0.0003831356,0.0001004756,0.00005220141,0.00008781371,0.0004045184,0.005365041],"genre_scores_gemma":[0.9076723,0.001323134,0.08884608,0.000156557,0.00005093697,0.00006239291,0.00007484615,0.00003722756,0.001776428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001543966,"threshold_uncertainty_score":0.003069878,"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."}}