Information‐centered representation of retrievals with limited degrees of freedom for signal: Application to methane from the Tropospheric Emission Spectrometer
Bibliographic record
Abstract
Remote measurements of trace gas profiles from nadir‐viewing instruments are often retrieved and/or reported on a fine grid containing more levels than the number of independent pieces of information in the measurement. Such profiles contain a priori information, which complicates interpretation. For scientific analyses of these data it is desirable to move to a representation in which measurement information is dominant and the influence of a priori information is minimal. Presented here is a postprocessing approach using a simple algorithm to transform each retrieved profile to an appropriate, geographically varying coarse grid. The representation is chosen such that the averaging kernel is close to unity for regions of the atmosphere where the retrieval has most information. The approach takes advantage of the sensitivity characterization allowed by retrieval on a fine grid, while reducing the influence of the a priori, accounting for spatial and temporal variations in the sensitivity of the measurement to the true atmosphere, and preserving obvious physical meaning in the end product. The example used to demonstrate the approach is the methane product from the Tropospheric Emission Spectrometer (TES), which contains 0.5–2.0 degrees of freedom for signal, depending on season and location. The TES methane has been postprocessed, and the end product has been compared with results from GEOS‐Chem, a global chemical model. Results show realistic latitudinal gradients from the TES data. Model/measurement differences also show large‐scale features over Indonesia that we attribute to tropical biomass burning in the summer/fall.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".