{"id":"W2903713534","doi":"10.1109/inertialsensors.2018.8577143","title":"Visual lnertial Hybridization Technique based on Beacons identified by Deep Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Beacon; Artificial intelligence; Computer vision; Computer science; Sight; Point (geometry); Line-of-sight; Visualization; Line (geometry); Position (finance); Sensor fusion; Real-time computing; Engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008823418,0.000119344,0.00009305646,0.0001053034,0.0001100586,0.00005885426,0.00005803342,0.00008463742,0.000253378],"category_scores_gemma":[0.00004873606,0.0001216466,0.00003156651,0.0001831482,0.00003011783,0.0000672367,0.000007186307,0.00011178,0.00008798594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004848978,"about_ca_system_score_gemma":0.000008478769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001526981,"about_ca_topic_score_gemma":0.00001348157,"domain_scores_codex":[0.9993165,0.00003382022,0.0001713869,0.0001545365,0.0001509312,0.0001727998],"domain_scores_gemma":[0.9997128,0.00003415169,0.0000219072,0.0001093136,0.00006651177,0.00005535166],"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":[0.000015408,0.00004440485,0.000259643,0.00002070116,0.00001157479,0.000002787029,0.00003745477,0.8272984,0.163662,0.0005168517,0.00339601,0.004734813],"study_design_scores_gemma":[0.0001466036,0.00008199789,0.00005994339,0.00001154015,0.000006159421,7.304757e-7,0.000008955441,0.7480998,0.2496821,0.00001681095,0.001764731,0.0001205657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01578272,0.000006724007,0.9768757,0.00003921074,0.0002029143,0.0001595779,0.000001393675,0.0005673107,0.006364449],"genre_scores_gemma":[0.9971844,0.000003693034,0.002010674,0.000111408,0.0001223739,0.0000145907,0.0001220116,0.0000415916,0.0003892677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9814017,"threshold_uncertainty_score":0.4960604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004070944475012695,"score_gpt":0.2142290435174909,"score_spread":0.2101580990424783,"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."}}