New and improved infra-red absorption cross sections and ACE-FTS retrievals of carbon tetrachloride (CCl4)
Bibliographic record
Abstract
Carbon tetrachloride (CCl 4 ) is one of the species regulated by the Montreal Protocol on account of its ability to deplete stratospheric ozone. As such, the inconsistency between observations of its abundance and estimated sources and sinks is an important problem requiring urgent attention (Carpenter et al., 2014) [5] . Satellite remote-sensing has a role to play, particularly limb sounders which can provide vertical profiles into the stratosphere and therefore validate stratospheric loss rates in atmospheric models. This work is in two parts. The first describes new and improved high-resolution infra-red absorption cross sections of carbon tetrachloride/dry synthetic air over the spectral range 700–860 cm −1 for a range of temperatures and pressures (7.5–760 Torr and 208–296 K) appropriate for atmospheric conditions. This new cross-section dataset improves upon the one currently available in the HITRAN and GEISA databases. The second describes a new, preliminary ACE-FTS carbon tetrachloride retrieval that improves upon the v3.0/v3.5 data products, which are biased high by up to ~20–30% relative to ground measurements. Making use of the new spectroscopic data, this retrieval also improves the microwindow selection, contains additional interfering species, and utilises a new instrumental lineshape; it will form the basis for the upcoming v4.0 CCl 4 data product.
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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 | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".