{"id":"W3127391403","doi":"10.1109/lra.2021.3060397","title":"UAV Localization Using Autoencoded Satellite Images","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Defence Research and Development Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Autoencoder; Satellite; Kernel (algebra); Computation; Representation (politics); Pattern recognition (psychology); Remote sensing; Deep learning; Geography; Algorithm; Mathematics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001346135,0.0003813485,0.0003922978,0.0002284894,0.0001314075,0.0004921314,0.0001195831,0.0003023082,0.00001217153],"category_scores_gemma":[0.00001802685,0.0004402364,0.0001032017,0.0002152416,0.00006020825,0.0001856603,0.00006386754,0.000323565,0.000005185148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001623636,"about_ca_system_score_gemma":0.00004391193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003342071,"about_ca_topic_score_gemma":0.000005999379,"domain_scores_codex":[0.9983965,0.0000735384,0.000543252,0.0003970952,0.0002967408,0.0002928927],"domain_scores_gemma":[0.9992336,0.00004008872,0.0001507195,0.0003485294,0.0001239725,0.0001030794],"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":[0.000001055251,0.00001312337,0.0002213368,0.0005000373,0.00006534902,0.00001692434,0.0002301839,0.9801944,0.01714055,0.0000875128,0.0003515283,0.001178036],"study_design_scores_gemma":[0.0001831286,0.000005425473,0.0006494912,0.000338288,0.0001010645,0.00001011467,0.00003415955,0.9931781,0.004862183,0.00009211442,0.00008223076,0.0004636441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08199385,0.0006336832,0.9144428,0.0003866408,0.001721114,0.0002529794,0.00001650999,0.0004919839,0.0000603914],"genre_scores_gemma":[0.9315972,0.001211477,0.06533438,0.0007567041,0.0003405459,0.00001070202,0.000591923,0.0001390318,0.00001802289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8496034,"threshold_uncertainty_score":0.9998049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620983881900805,"score_gpt":0.2264389787113051,"score_spread":0.210229139892297,"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."}}