{"id":"W3208836193","doi":"10.1109/robot.2010.5509397","title":"Hybrid aerial and scansorial robotics","year":2010,"lang":"en","type":"article","venue":"","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency","keywords":"Climbing; Robot; Robotics; Computer science; Aerospace engineering; Drone; Plane (geometry); Artificial intelligence; Simulation; Aeronautics; Engineering; Structural engineering; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010967,0.0003764798,0.0003154062,0.0003603045,0.0003296819,0.0007128912,0.000811163,0.0004522363,0.005012346],"category_scores_gemma":[0.0001894954,0.000196259,0.0002840455,0.0002191907,0.000509058,0.0007266603,0.001497844,0.0002844803,0.001160216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000172275,"about_ca_system_score_gemma":0.0003332661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007573374,"about_ca_topic_score_gemma":0.002112976,"domain_scores_codex":[0.999828,0.00001864814,0.000004947427,0.00003827451,0.0000832583,0.00002673172],"domain_scores_gemma":[0.9998828,0.00002133641,0.00001518303,0.00003998522,0.00002362041,0.0000170788],"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":[0.000412024,0.0001801573,0.003104344,0.000346875,0.0001391923,0.0009979901,0.000361717,0.1025706,0.2341821,0.1167684,0.009334839,0.5316017],"study_design_scores_gemma":[0.00009379764,0.0009246161,0.003582287,0.00008701643,0.00008329155,0.002045749,0.0003020012,0.7988833,0.05846131,0.04713461,0.08831125,0.00009077525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1209025,0.0007046146,0.7849987,0.0003296169,0.0001925503,0.0001435236,0.0001942627,0.002211303,0.09032293],"genre_scores_gemma":[0.7286029,0.0003457732,0.2348917,0.0001989071,0.00006474434,0.0001945392,0.0002396013,0.0001249956,0.03533686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005012346,"threshold_uncertainty_score":0.01676792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003699120006639486,"score_gpt":0.1773589366541898,"score_spread":0.1736598166475503,"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."}}