A Rapid Retrieval Methodology Based On The Spectrally Integrated Voigt Function For Space Observation Spectral Radiance Data
Bibliographic record
Abstract
In our recent publication, we proposed the application of the spectrally integrated Voigt function (SIVF) to a line-by-line (LBL) radiative transfer modelling1. We applied the GENSPECT LBL radiative transfer model that utilizes the HITRAN database to generate synthetic spectral data due to thermal or solar radiation of the Earth or planetary atmosphere2. It has been shown that the SIVF methodology enables the computation of a LBL radiative transfer at reduced spectral resolution model without loss in accuracy. In contrast to the traditional method of computation, the SIVF implementation accounts for the area under the Voigt function between adjacent grid points resulting in well-preserved shape of a spectral radiance even at low spectral resolution. This significant advantage of the SIVF methodology can be applied in the rapid retrieval of the space observation data, required for real-time control and decision making in future generation of the Argus3 remote-sensing microspectrometers. The spectrally integrated methodology can be generalized to other linebroadening profiles, such as Galatry, Rautian-Sobelman or speed dependent profiles, to prevent underestimation of the spectral radiance that always occurs at reduced spectral resolution1 in any LBL radiative transfer model using a traditional method of computation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".