Comment on amt-2021-300
Bibliographic record
Abstract
Abstract. Nineteen limb-viewing data sets (occultation, passive thermal, and UV scattering) and two nadir upper tropospheric humidity (UTH) data sets are intercompared and also compared to frost-point hygrometer balloon sondes. The upper troposphere considered here covers the pressure range from 300–100 hPa. UTH is a challenging measurement, because concentrations vary between 2–1000 ppmv (parts per million by volume), with sharp changes in vertical gradients near the tropopause. Cloudiness in this region also makes the measurement challenging. The atmospheric temperature is also highly variable ranging from 180–250 K. The assessment of satellite-measured UTH is based on coincident comparisons with balloon frost-point hygrometer sondes, multi-month mapped comparisons, zonal mean time series comparisons, and coincident satellite-to-satellite comparisons. While the satellite fields show similar features in maps and time series, quantitatively they can differ by a factor of 2 in concentration, with strong dependencies on the amount of UTH. Additionally, time-lag response-corrected Vaisala RS92 radiosondes are compared to satellites and the frost-point hygrometer measurements. In summary, most satellite data sets reviewed here show on average ∼30 % agreement amongst themselves and frost-point data but with an additional ∼30 % variability about the mean bias. The Vaisala RS92 sonde, even with a time-lag correction, shows poor behavior for pressures less than 200 hPa.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.180 | 0.177 |
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".