Information content in reflected signals during GPS Radio Occultation observations
Bibliographic record
Abstract
The possibility of extracting useful information about the state of the lower troposphere from the surface reflections that are often detected during GPS radio occultations (GPSRO) is explored. The clarity of the reflection is quantified, and can be related to properties of the surface and the low troposphere. The reflected signal is often clear enough to show good phase coherence, and can be tracked and processed as an extension of direct non-reflected GPSRO atmospheric profiles. A profile of bending angle vs. impact parameter can be obtained for these reflected signals, characterized by impact parameters that are below the apparent horizon, and that is a continuation at low altitude of the standard non-reflected bending angle profile. If there were no reflection, these would correspond to tangent altitudes below the local surface, and in particular below the local mean sea level. A forward operator is presented, for the evaluation of the bending angle of reflected GPSRO signals, given atmospheric properties as described by a numerical weather prediction system. The operator is an extension, at lower impact parameters, of standard bending angle operators, and reproduces both the direct and reflected sections of the measured profile. It can be applied to the assimilation of the reflected section of the profile as supplementary data to the direct section. Although the principle is also applicable over land, this paper is focused on ocean cases, where the topographic height of the reflecting surface, the sea level, is better known a priori.
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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".