Bibliographic record
Abstract
By employing the improved T42L5 spectral model and utilizing the ECMWF data covering the period from 1 Julyto 7 July 1982,a numerical research on the formation of the Ural blocking system has been made.The results show thatthe model forecasts for the upstream U ral area turn out to be worse if the dynamic effect of the Qinghai-Xizang Plateauis not considered.The correlation coefficient between the model forecasts and observed 500 hPa geopotential heightanomaly decreases by 9% for the 5-day mean,and their averaged root mean square (RMS) error increases 15 m.Due tothe dynamic effect of the Plateau,the trough being on the northwest of the Plateau is barricaded and turns to be atransversal trough.Consequently southwest flow occurs along the northwest of the Plateau in front of the trough,whilenortheast flow prevails over the west of the trough,causing the formation of the blocking high over the Ural area.Whenthe dynamic effect of the Plateau is not taken into consideration,the trough develops and moves southeastward and theUral blocking high changes into a migratory high.All these result in the failure of the simulation.The dynamic effect ofthe Plateau helps to increase the negative vorticities over the Plateau and its north periphery as well as the Ural area,andalso helps to increase the positive vorticities over the Black Sea and the Caspian Sea area.On the other hand,thethermodynamic effect mainly influences the Plateau and its downstream area and plays an less important role in theformation of the blocking high over the upstream Ural area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.991 | 0.992 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".