{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002683279,0.0002771049,0.000402116,0.0001037003,0.0001469778,0.000159221,0.0001111195,0.0001621196,0.000002700516],"category_scores_gemma":[0.000008210789,0.0002773253,0.00007234036,0.0002574614,0.00001687706,0.0001581511,0.000005342998,0.0001564886,0.00004232551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000346824,"about_ca_system_score_gemma":0.00004200398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002744766,"about_ca_topic_score_gemma":0.0000461977,"domain_scores_codex":[0.9983596,0.0000266147,0.000346635,0.0002973503,0.0001644715,0.0008052994],"domain_scores_gemma":[0.9993833,0.00004629371,0.00006753562,0.000279977,0.00008886743,0.0001340641],"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":[0.00002970154,0.00005312,0.0001973295,0.001455564,0.0001138553,0.00000995875,0.000340019,0.9245535,0.04802407,0.005687402,0.01754467,0.001990872],"study_design_scores_gemma":[0.001462659,0.0004348787,0.0001542113,0.0004784968,0.00005803534,0.00008398978,0.0006019537,0.9476359,0.004041463,0.00003151842,0.04438815,0.00062875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3298765,0.01078977,0.6474165,0.0002011888,0.002143834,0.004218104,0.00007050665,0.001431348,0.003852277],"genre_scores_gemma":[0.9970903,0.0002536944,0.0001002713,0.00003754469,0.0003316964,0.0001610629,0.00004996419,0.00005789373,0.001917594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6672139,"threshold_uncertainty_score":0.9999679,"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."}}