{"id":"W1887247996","doi":"10.5194/isprsarchives-xl-1-w4-337-2015","title":"PERFORMANCE CHARACTERISTIC MEMS-BASED IMUs FOR UAVs NAVIGATION","year":2015,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Norges Forskningsråd; Norges Teknisk-Naturvitenskapelige Universitet","keywords":"Global Positioning System; Inertial measurement unit; Mobile mapping; Computer science; Software; Real-time computing; Inertial navigation system; Pipeline (software); Data acquisition; Systems engineering; Embedded system; Engineering; Artificial intelligence; Inertial frame of reference; Telecommunications","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":[0.0002113045,0.0005423473,0.0003009151,0.0003654867,0.0002324562,0.0003115005,0.0003273408,0.0005018842,0.001957195],"category_scores_gemma":[0.0004674989,0.0001400727,0.0001344259,0.0001957193,0.00009780648,0.0002826063,0.0001850614,0.0001747442,0.0007501707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347465,"about_ca_system_score_gemma":0.0001180358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006523149,"about_ca_topic_score_gemma":0.0009621213,"domain_scores_codex":[0.9997044,0.00004318611,0.00001346061,0.00004697617,0.0001595026,0.00003251432],"domain_scores_gemma":[0.9996421,0.00007782243,0.00006292865,0.00004338556,0.000156606,0.00001718129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00110957,0.0001148775,0.02093522,0.000684235,0.0001215365,0.0004057302,0.0004188165,0.01904791,0.816833,0.001024085,0.004001918,0.1353033],"study_design_scores_gemma":[0.00005297742,0.003201726,0.06031279,0.00008616552,0.0001223905,0.0006803895,0.00030348,0.1835701,0.7364895,0.0002898387,0.0148338,0.00005686135],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9459351,0.002465377,0.03918481,0.0002676607,0.0002942221,0.00008043977,0.0005426807,0.001093479,0.01013621],"genre_scores_gemma":[0.9918244,0.0001999167,0.005673739,0.00004254923,0.00001772743,0.00003103959,0.0001121785,0.00001660709,0.002081727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001957195,"threshold_uncertainty_score":0.006547511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138379529107089,"score_gpt":0.2599454599321812,"score_spread":0.2385616646411103,"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."}}