{"id":"W2765577898","doi":"10.3390/robotics6040028","title":"A High-Fidelity Energy Efficient Path Planner for Unmanned Airships","year":2017,"lang":"en","type":"article","venue":"Robotics","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Terrain; Computer science; Turning radius; Energy (signal processing); Energy consumption; Grid; Acceleration; Aerospace engineering; Control theory (sociology); Simulation; Engineering; Mathematics; Artificial intelligence; Physics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002257015,0.0003126784,0.0002543688,0.0002323027,0.0002250549,0.000290749,0.0003865031,0.0002832934,0.001466548],"category_scores_gemma":[0.0008227826,0.0002110788,0.0001498173,0.0001752927,0.0002404449,0.0004671744,0.0003859876,0.0003488051,0.0001515885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003529248,"about_ca_system_score_gemma":0.0009146289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006682813,"about_ca_topic_score_gemma":0.008469439,"domain_scores_codex":[0.9998739,0.00002524261,0.000003965563,0.00001781179,0.00006517959,0.0000139753],"domain_scores_gemma":[0.9997897,0.0001030512,0.00002675732,0.00002745153,0.00004020245,0.00001290609],"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":[0.0000282247,0.00002204991,0.0001941116,0.00002076108,0.000004990229,0.00003440144,0.00003044774,0.9680806,0.003038185,0.00181952,0.0002220883,0.0265046],"study_design_scores_gemma":[0.000007133552,0.00003408411,0.0001132909,0.000002755601,0.000001694847,0.00001413586,0.00001073227,0.9972745,0.001298026,0.0005604134,0.0006808097,0.000002325335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07692546,0.0001112844,0.918321,0.00006418413,0.0000123717,0.00008545646,0.00009169398,0.0006964198,0.003692114],"genre_scores_gemma":[0.6650464,0.0001115392,0.3322351,0.00001562054,0.000003979371,0.00009202801,0.0001601503,0.00008519222,0.002250034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006682813,"threshold_uncertainty_score":0.01328784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147039062530033,"score_gpt":0.2091078633144723,"score_spread":0.194403957061469,"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."}}