{"id":"W3027951168","doi":"10.1088/1361-6501/ab955b","title":"Determination and analysis of front-end and correlator-spacing-induced biases for code and carrier phase observations","year":2020,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"GNSS positioning and interference","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Front and back ends; Phase (matter); Code (set theory); Computer science; Optics; Physics; Programming language","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":[0.0007237969,0.0002972551,0.0002240753,0.0007389956,0.000199577,0.0003605236,0.0003231132,0.0003109266,0.0003908955],"category_scores_gemma":[0.002570421,0.0001409634,0.0002513986,0.0007817702,0.0001522756,0.0002965946,0.0002638683,0.0002498273,0.0001704762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799597,"about_ca_system_score_gemma":0.0003616285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002131269,"about_ca_topic_score_gemma":0.003031313,"domain_scores_codex":[0.9993913,0.00007878769,0.00003127529,0.0001111191,0.0003340036,0.00005334652],"domain_scores_gemma":[0.9980149,0.0007053927,0.0002456808,0.0002354457,0.0007672904,0.00003131352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007448535,0.0002374973,0.3407461,0.0003581485,0.0002739395,0.000397929,0.0003125481,0.1168087,0.3675404,0.001092403,0.0009020214,0.1705856],"study_design_scores_gemma":[0.0000391875,0.0002595894,0.3259368,0.00004781203,0.0001363786,0.0002492774,0.000109488,0.3080443,0.3624322,0.00037519,0.002301399,0.00006839984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374517,0.0001651362,0.06033274,0.00001941342,0.00002807303,0.00002922105,0.000471008,0.0002933703,0.001209432],"genre_scores_gemma":[0.9770671,0.00008276883,0.02146783,0.00002091208,0.000007438788,0.00003316633,0.0008016526,0.00005442159,0.0004646882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002131269,"threshold_uncertainty_score":0.004237771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1075789460213839,"score_gpt":0.2846894254503153,"score_spread":0.1771104794289314,"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."}}