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Record W2386540315

Application research of four cold regions land surface and hydrological model to Qinghai-tibet plateau frozen soil region

2012· article· en· W2386540315 on OpenAlexaboutno aff
Yibo Wang

Bibliographic record

VenueJournal of Water Resources and Water Engineering · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPlateau (mathematics)PermafrostVegetation (pathology)Frost (temperature)Physical geographyEnvironmental scienceHydrology (agriculture)GeologyGeographyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.260
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Water Resources and Water EngineeringSame topicClimate change and permafrostFrench-language works237,207