Detecting the recovery of total column ozone
Bibliographic record
Abstract
International agreements for the limitation of ozone‐depleting substances have already resulted in decreases in concentrations of some of these chemicals in the troposphere. Full compliance and understanding of all factors contributing to ozone depletion are still uncertain; however, reasonable expectations are for a gradual recovery of the ozone layer over the next 50 years. Because of the complexity of the processes involved in ozone depletion, it is crucial to detect not just a decrease in ozone‐depleting substances but also a recovery in the ozone layer. The recovery is likely to be detected in some areas sooner than others because of natural variability in ozone concentrations. On the basis of both the magnitude and autocorrelation of the noise from Nimbus 7 Total Ozone Mapping Spectrometer ozone measurements, estimates of the time required to detect a fixed trend in ozone at various locations around the world are presented. Predictions from the Goddard Space Flight Center (GSFC) two‐dimensional chemical model are used to estimate the time required to detect predicted trends in different areas of the world. The analysis is based on our current understanding of ozone chemistry, full compliance with the Montreal Protocol and its amendments, and no intervening factors, such as major volcanic eruptions or enhanced stratospheric cooling. The results indicate that recovery of total column ozone is likely to be detected earliest in the Southern Hemisphere near New Zealand, southern Africa, and southern South America and that the range of time expected to detect recovery for most regions of the world is between 15 and 45 years. Should the recovery be slower than predicted by the GSFC model, owing, for instance, to the effect of greenhouse gas emissions, or should measurement sites be perturbed, even longer times would be needed for detection.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".