Electron number density profiles derived from radio occultation on the CASSIOPE spacecraft
Bibliographic record
Abstract
Abstract This paper presents electron number density profiles derived from high‐resolution Global Positioning System (GPS) radio occultation (RO) observations performed using the Enhanced Polar Outflow Probe payload on the high inclination CAScade, Smallsat and IOnospheric Polar Explorer (CASSIOPE) spacecraft. We have developed and applied a novel inverse Abel transform algorithm on high rate RO total electron content measurements performed along GPS to CASSIOPE radio links to recover electron density profiles. The high‐resolution density profiles inferred from the CASSIOPE RO are (1) in very good agreement with density profiles estimated from ionosonde data, measured over stations nearby to the latitude and longitude of the RO tangent points; (2) in good agreement with density profiles inferred from GPS RO measured by the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and (3) in general agreement with density profiles estimated using the International Reference Ionosphere climatological model. Using both CASSIOPE and COSMIC RO observations, we identify, for the first time, that there exist differences in the characteristics of the electron number density profiles retrieved over landmasses and oceans. The density profiles over oceans exhibit widespread values and scale heights compared to density profiles over landmasses. We provide an explanation for the ocean‐landmass discrepancy in terms of the unique wave coupling mechanisms operating over oceans and landmasses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".