Bibliographic record
Abstract
Abstract. Variations in the isotopological composition of water vapour are fundamental for understanding the relative importance of different mechanisms of water vapor transport from the tropical upper troposphere to the lower stratosphere. Previous comparisons obtained from observations of H2O and HDO by satellite instruments showed discrepancies. In this work, newer versions of H2O and HDO retrievals from Envisat/MIPAS are compared with data derived from SCISAT/ACE-FTS. Specifically, MIPAS-IMK V5, MIPAS-ESA V8, and ACE-FTS V4.1/4.2 for the common period from February 2004 to April 2012 are compared for the first time through a profile-to-profile approach and comparison based on climatological structures. Stratospheric H2O and HDO global average coincident profiles reveal good agreement. The smallest biases are found between 20 and 30 km, and the largest biases are exhibited around 40 km both in absolute and relative terms. For HDO, biases between -8.6–10.6 % are observed among the three databases in the altitudes of 16 to 30 km. However, around 40 km, ACE-FTS agrees better to MIPAS-IMK than MIPAS-ESA, with biases of -4.8 % and -37.5 %, respectively. The HDO bias between MIPAS-IMK and MIPAS ESA is 28.1 % at this altitude. The meridional cross-sections of H2O and HDO exhibit the expected distribution that has been established in previous studies. The tape recorder signal is present in H2O and HDO for the three databases with slight quantitative differences. The meridional cross-sections of δD are in good agreement with the previous version of MIPAS-IMK and ACE-FTS data. In the temporal δD variations, the results suggest that in the current data versions, the calculated isotopic composition (δD) from MIPAS-IMK aligns more closely with expected stratospheric behavior for the entire stratosphere. Nevertheless, there are differences in the climatological δD composites between databases that could lead to different interpretations regarding the water vapor transport processes toward the stratosphere, so it is important to intercompare these δD observations.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.194 | 0.152 |
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".