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Record W1824336683 · doi:10.1657/aaar00c-13-128

Temperature-Dependent Adjustments of the Permafrost Thermal Profiles on the Qinghai-Tibet Plateau, China

2015· article· en· W1824336683 on OpenAlexaff
William A. Gough, Tonghua Wu

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersChinese Academy of Sciences
KeywordsPermafrostPlateau (mathematics)Global warmingThermalGeologyClimatologyClimate changeLatent heatAtmospheric sciencesPhysical geographyEnvironmental scienceGeographyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Using continuous data obtained from 17 monitoring sites, the permafrost temperature profiles and the depths of zero annual amplitude (DZAA) on the Qinghai-Tibet Plateau are examined. Permafrost thermal trumpet curves are generally narrow and the DZAAs are generally shallow in warm permafrost regions, especially at sites where the permafrost temperature is close to 0 °C. The observed DZAAs in warm permafrost regions are indeed generally less than 7.0 m and for three sites less than 4.0 m. In low-temperature permafrost areas, the situation is reversed: the thermal trumpet curves are generally wide and the DZAAs are generally deep. Theoretical and numerical analyses clearly show there is a causal relationship between permafrost warming and the decrease of the DZAA. Latent heat effects are buffering the increase of permafrost temperature and result in narrow thermal trumpet curves and shallow DZAAs. Based on observations and numerical analyses, this research suggests that most of the permafrost on the Qinghai-Tibet Plateau is undergoing internal thaw and the latent heat effects have important implications on the permafrost thermal regime. The temperature-dependent adjustments in permafrost will promote both the downward and upward degradation of permafrost as a result of climate warming.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.304
Teacher spread0.212 · 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.

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

Citations34
Published2015
Admission routes1
Has abstractyes

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