Enhanced Polar Outflow Probe Ionospheric Radio Occultation Measurements at High Latitudes: Receiver Bias Estimation and Comparison With Ground‐Based Observations
Bibliographic record
Abstract
Abstract This paper presents validation of ionospheric Global Positioning System (GPS) radio occultation measurements of the GPS Attitude, Positioning, and Profiling Experiment occultation receiver (GAP‐O). GAP is one of eight instruments comprising the Enhanced Polar Outflow Probe (e‐POP) instrument suite on board the Cascade Smallsat and Ionospheric Polar Explorer (CASSIOPE) satellite. One of the main error sources for certain GAP‐O data products is the receiver differential code bias (rDCB). A minimization of standard deviations (MSD) technique has shown the most promise for rDCB estimation, with estimates ranging primarily from −40 to −28 total electron content units (TECU = 1016 el m−2; 21.6 to 15.1 ns), including a long‐term decrease in rDCB magnitude and variability over the first 3 years of instrument operation. In application of the MSD method, the sensitivity of bias estimates to ionospheric shell height are as large as 4.5 TECU per 100 km. MSD calculations also agree well with the “assumption of zero topside TEC” method for rDCB estimate at satellite apogee. Bias‐corrected topside TEC of GAP‐O was validated by statistical comparison with topside TEC obtained from ground‐based GPS TEC and ionosonde measurements. Although GAP‐O and ground‐based topside TEC had similar variability, GAP‐O consistently underestimated the ground‐derived topside TEC by up to 7 TECU. Ionospheric electron density profiles obtained from Abel inversion of GAP‐O occultation TEC showed good agreement with F region densities of ground‐based incoherent scatter radar measurements. Comparison of GAP‐O and ionosonde measurements revealed correlation coefficients of 0.78 and 0.79, for peak F region density and altitude, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".