Validation of long‐term measurements of water vapor from the midstratosphere to the mesosphere at two Network for the Detection of Atmospheric Composition Change sites
Bibliographic record
Abstract
We present measurements from the Water Vapor Millimeter‐wave Spectrometer (WVMS) instruments at Table Mountain, California (34.4°N, 242.3°E), and Mauna Loa, Hawaii (19.5°N, 204.4°E), and highlight the extended altitude range of the measurements at these sites, which now provide measurements down to 26 km. We show that this extended altitude range has been acquired without disturbing the existing long‐term WVMS data set at Mauna Loa. Validation of the successful transition is provided by comparing WVMS measurements with coincident satellite measurements from the Aura Microwave Limb Sounder (MLS), the Atmospheric Chemistry Experiment, and the Michelson Interferometer for Passive Atmospheric Sounding. At the lowest altitudes where WVMS measurements are possible, we also compare with frost‐point hygrometer balloon measurements. The water vapor mixing ratios measured at 50 km over Mauna Loa are the highest ever reported in the WVMS (since 1996) or MLS (since 2004) time series. Particularly encouraging for the new 26 km WVMS measurements is that they indicate an increase between 2010 and 2011 that is comparable to that observed by other instruments. This shows that these measurements are sensitive to variations at this altitude and that the instrumental baseline remains stable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".