Recurrence of cyclonic events over the Sakha Republic (Yakutia) in summer months
Bibliographic record
Abstract
Extratropical cyclones over the Sakha Republic (Yakutia) bring rains and reduce the probability of forest fires, but can cause fast rainflood events caused by heavy precipitation. In this study, recurrence of cyclonic events over the Sakha Republic (Yakutia) in summer months between 1950 and 2022 is considered. Recurrence of cyclonic events was defined as the count of cyclone centers over Yakutia or subregions at a standard time of observations. Extratropical cyclone centers database from University of Manitoba (Canada) based on ERA5 reanalysis was used in this study. Cyclonic weather over the territory of the Sakha Republic (Yakutia) persists for 27.5 days on average. The largest count of cyclonic events is noted in the North-Eastern and North-Western sub-regions, while the smallest is in the Southern and Central sub-regions. Across summer months, cyclones are most active in June in all regions except for the Southern sub-region. Here, increased July cyclonic activity is determined by conditions favorable for atmospheric blocking, limited zonal transport promoting the northward intrusion of southern cyclones. There is no pronounced trend in the frequency of occurrence of cyclones except for some regions and months: Western Area, June, increasing; the Sakha Republic (Yakutia), August, decreasing. An absolute minimum in the number of cyclonic events was established for 2019-2021, which, as we believe, was one of the main reasons for the maximum of forest fire activity in the Sakha Republic (Yakutia) observed in these years, along with the Lena River extremely low flows of 2019. An analysis of the circulation conditions leading to such extremes will make it possible to assess the risk of recurrence of similar situations under the future climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".