The impact of nonuniform sampling on stratospheric ozone trends derived from occultation instruments
Bibliographic record
Abstract
Abstract. This paper applies a recently developed technique for deriving long-term trends in ozone from sparsely sampled data sets to multiple occultation instruments simultaneously without the need for homogenization. The technique can compensate for the nonuniform temporal, spatial, and diurnal sampling of the different instruments and can also be used to account for biases and drifts between instruments. These problems have been noted in recent international assessments as being a primary source of uncertainty that clouds the significance of derived trends. Results show potential “recovery” trends of ∼ 2–3 % decade−1 in the upper stratosphere at midlatitudes, which are similar to other studies, and also how sampling biases present in these data sets can create differences in derived recovery trends of up to ∼ 1 % decade−1 if not properly accounted for. Limitations inherent to all techniques (e.g., relative instrument drifts) and their impacts (e.g., trend differences up to ∼ 2 % decade−1) are also described and a potential path forward towards resolution is presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".