New and improved infrared absorption cross sections for trichlorofluoromethane (CFC-11)
Bibliographic record
Abstract
Abstract. Trichlorofluoromethane (CFC-11), a widely used refrigerant throughout much of the twentieth century and a very potent (stratospheric) ozone depleting substance (ODS), is now banned under the Montreal Protocol. With a long atmospheric lifetime, it will only slowly degrade in the atmosphere, so monitoring its vertical concentration profile using infrared-sounding instruments, thereby validating stratospheric loss rates in atmospheric models, is of great importance; this in turn requires high quality laboratory spectroscopic data. This work describes new high-resolution infrared absorption cross sections of trichlorofluoromethane/dry synthetic air over the spectral range 710–1290 cm−1, determined from spectra recorded using a high-resolution Fourier transform spectrometer (Bruker IFS 125HR) and a 26-cm-pathlength cell. Spectra were recorded at resolutions between 0.01 and 0.03 cm−1 (calculated as 0.9/MOPD; MOPD = maximum optical path difference) over a range of temperatures and pressures (7.5–760 Torr and 192–293 K) appropriate for atmospheric conditions. This new cross-section dataset improves upon the one currently available in the HITRAN and GEISA databases.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.004 |
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".