Bibliographic record
Abstract
Spectroscopic methods are used to study planetary and stellar atmospheres. The information obtained from spectroscopic studies provides insight into atmospheric compositions and dynamics, which can be used to model and characterize atmospheres and climates. Laboratory-recorded absorption cross-sections are needed to interpret the recorded spectra of planets and stars. High resolution ethane, neopentane, propene, and n-butane spectra have been recorded, and absorption cross-sections have been provided for different temperatures and total pressures with different broadening gases, including hydrogen, helium, and nitrogen. The Atmospheric Chemistry Experiment (ACE) satellite orbits Earth and records spectra through solar occultation limb observations. HOCl is a chlorine reservoir molecule found in the Earth’s stratosphere and is also responsible for polar ozone destruction. HOCl retrievals have been developed using the spectra recorded by the ACE. Analysis of ACE HOCl VMR data globally for the entire mission (2004-2020) shows a decreasing trend attributed to the Montreal Protocol’s success. In ACE version 5.0 data, HFC-32 is a new molecule added. Global altitude-latitude VMR profiles for 2004 to 2020 were studied, and HFC-32 abundances are increasing exponentially. LaO and YO are characteristic features in the spectra of cool S-type stars. The B2Σ +-X2Σ + and A2Π-X2Σ + transitions of LaO and the B2Σ +- X 2Σ + transition of YO were rotationally analyzed to provide spectroscopic constants and, more importantly, a line list suitable for simulating stellar spectra.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".