Comparison of the CMAM30 data set with ACE-FTS and OSIRIS: polar regions
Bibliographic record
Abstract
Abstract. CMAM30 is a 30-year data set extending from 1979 to 2010 that is generated using a version of the Canadian Middle Atmosphere Model (CMAM) in which the winds and temperatures are relaxed to the Interim Reanalysis product from the European Centre for Medium-Range Weather Forecasts (ERA-Interim). The data set has dynamical fields that are very close to the reanalysis below 1 hPa and chemical tracers that are self-consistent with respect to the model winds and temperature. The chemical tracers are expected to be close to actual observations. The data set is here compared to two satellite records – the Atmospheric Chemistry Experiment Fourier transform spectrometer and the Odin Optical Spectrograph and Infrared Imaging System – for the purpose of validating the temperature, ozone, water vapour and methane fields. Data from the Aura microwave limb sounder are also used for validation of the chemical processing in the polar vortex. It is found that the CMAM30 temperature is warmer by up to 5 K in the stratosphere, with a low bias in the mesosphere of ~ 5–15 K. Ozone is reasonable (±15 %), except near the tropopause globally and in the Southern Hemisphere winter polar vortex. Water vapour is consistently low by 10–20 %, with corresponding high methane of 10–20 %, except in the Southern Hemisphere polar vortex. Discrepancies in this region are shown to stem from the treatment of polar stratospheric cloud formation in the model.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".