The forecast of erythemal UV irradiance over the territory of Northern Eurasia according to the INM-RSHU chemical-climate model
Bibliographic record
Abstract
In this work, the impact of various factors on the total ozone column and erythemal UV radiation (Qery) in the territory of Northern Eurasia for the period from 1979 to 2059 based on the calculations of the chemical-climate model INM-RHSU is analyzed. The sensitivity of ozone recovery to the setting of different input data on sea surface temperature (SST) is estimated. Depending on the SST datasets, there are significant differences in ozone trends. A possible mechanism that explains the reasons for these differences is examined. The numerical experiment with the only change in ozone depleting substances according to Montreal protocol showed the ozone recovery and, as a result, Qery reduction, but this recovery is not linear. During the 2016-2020 period we estimated the 2-5% increase in Qery values relative to the baseline period (1979-1983) with about 6% maximum over Russian polar region. During the 2035-2039 period the Qery change against 1979-1983 period is about zero, during the 2055-2059 period we obtained the decrease of about 4-6% over Northern Asia and 6-8% over Northern Europe These changes corresponded to the noticeable boundary location shift of UV resources, which determine UV radiation impact on human health. The most significant changes will be observed in spring and summer: the UV deficiency zone will be expanded in the north and the UV excess zone over northern seas will be reduced in the south.
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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 | 0.000 |
| 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) | 0.001 | 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".