{"id":"W2133315771","doi":"10.1109/maes.2009.4839272","title":"Italian low cost GNSS/INS system suitable for mobile mapping","year":2009,"lang":"en","type":"article","venue":"IEEE Aerospace and Electronic Systems Magazine","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"GNSS applications; Global Positioning System; Mobile mapping; Photogrammetry; Computer science; Cadastre; Inertial navigation system; Reliability (semiconductor); Inertial measurement unit; Real-time computing; Embedded system; Engineering; Systems engineering; Telecommunications; Geography; Artificial intelligence; Orientation (vector space)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005440284,0.0009771941,0.0007912431,0.001047881,0.0006607909,0.0006194226,0.0005805746,0.0009386995,0.009057613],"category_scores_gemma":[0.0003179692,0.0002931905,0.0003339171,0.0009817549,0.0003859122,0.0004422345,0.0009611795,0.0005603606,0.01230112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005750931,"about_ca_system_score_gemma":0.0009888522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001290604,"about_ca_topic_score_gemma":0.001765758,"domain_scores_codex":[0.9993939,0.00008334748,0.0000246394,0.0001396705,0.0002854931,0.00007290275],"domain_scores_gemma":[0.9998055,0.00001000188,0.00001643273,0.00005290677,0.00007327431,0.00004186444],"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":[0.001735766,0.0002966594,0.01366651,0.00109079,0.0001041933,0.001475153,0.000408088,0.008883569,0.4504105,0.008147786,0.08073515,0.4330458],"study_design_scores_gemma":[0.000602525,0.001668879,0.052726,0.0001103395,0.0002556184,0.003844021,0.000120735,0.03834479,0.09500355,0.001704288,0.8054553,0.0001639292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2076479,0.003688518,0.4563297,0.001638423,0.0025568,0.002822022,0.01127084,0.04295961,0.2710863],"genre_scores_gemma":[0.5090757,0.001285149,0.3252259,0.0009816649,0.000810572,0.00158686,0.02560629,0.00111804,0.1343099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009057613,"threshold_uncertainty_score":0.03030074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007039810922997799,"score_gpt":0.2022566015221579,"score_spread":0.1952167905991601,"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."}}