{"id":"W3209641071","doi":"10.32920/ryerson.14662584.v1","title":"Integration of precise point positioning and reduced inertial sensors system","year":2021,"lang":"en","type":"preprint","venue":"","topic":"GNSS positioning and interference","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Global Positioning System; Precise Point Positioning; Computer science; Real-time computing; Inertial navigation system; Inertial measurement unit; Kalman filter; Code (set theory); BeiDou Navigation Satellite System; GPS signals; Positioning system; GPS/INS; GNSS applications; Real Time Kinematic; Assisted GPS; Point (geometry); Telecommunications; Inertial frame of reference; Artificial intelligence","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":[0.00008909339,0.0001808336,0.0002806129,0.000105159,0.00002920671,0.0001080126,0.00007520099,0.0001904728,0.00002542994],"category_scores_gemma":[0.00002527358,0.0001785339,0.00006728704,0.00005737927,0.0000235544,0.00008381149,0.000104525,0.0003247203,0.000002636684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009554096,"about_ca_system_score_gemma":0.0000231897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001627316,"about_ca_topic_score_gemma":0.00001105265,"domain_scores_codex":[0.9991583,0.00004207126,0.0003546191,0.0002215632,0.0001127786,0.0001106273],"domain_scores_gemma":[0.9994952,0.00002669056,0.00006390557,0.0002265677,0.0001398894,0.00004771365],"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":[0.00002252507,0.00003124999,0.00002544339,0.001907355,0.0001800807,0.000008466155,0.003632187,0.1022045,0.8868839,0.002319737,0.0004574875,0.002327102],"study_design_scores_gemma":[0.0001669816,0.00004410603,0.001339826,0.004909041,0.00007545586,0.00006721311,0.001677096,0.4156156,0.5757293,0.00005177431,0.000002953388,0.0003205683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9549767,0.0002488098,0.02370845,0.00002373736,0.0006492361,0.0001393197,0.00001413502,0.0002935986,0.01994606],"genre_scores_gemma":[0.9943506,0.00004000578,0.00528059,0.000003455164,0.00007188687,0.00002330547,0.0001072057,0.00002334596,0.000099652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3134111,"threshold_uncertainty_score":0.7280401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170008374215199,"score_gpt":0.2167832228241757,"score_spread":0.2050831390820237,"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."}}