Application research of four cold regions land surface and hydrological model to Qinghai-tibet plateau frozen soil region
Bibliographic record
Abstract
In recent years,the climatic environment of Qinghai-Tibet plateau frozen soil regions is becoming worse and worse year by year,which mainly consists of obvious permafrost decrease,more thickness of the frost soil active layer and degeneration of the vegetation.Climatic,water and ecological environment of this region is focused by researchers from many countries and districts.The ability of land surface model is approved by lots of scientists and it is emphasis of cold regions research that how to construct cold regions land surface model.This paper choose four land surface model(SHAW、COUPMODEL、EASS、CRHM) from USA,Western Europe and Canada(including two) and analysis principle and physical mechanisms of four models.At the same time,brief introduction of application in cold regions of four models,some models have been used to the Qinghai-Tibet plateau frozen soil regions and made quite a good simulation results,is included in this paper.By comparing four models,the paper found individual advantages,disadvantages and thinks that four models can be used on the Qinghai-Tibet plateau frozen soil regions individually and it is a significant approach to solve land surface problems of Qinghai-Tibet plateau,even global frozen soil regions as several models couple each other.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".