MétaCan
Menu
Back to cohort
Record W2293825804 · doi:10.5194/tc-10-1591-2016

Effects of stratified active layers on high-altitude permafrost warming:a case study on the Qinghai–Tibet Plateau

2016· article· en· W2293825804 on OpenAlexaff
Xicai Pan, Yanping Li, Qihao Yu, Xiaogang Shi, Daqing Yang, Kurt Roth

Bibliographic record

Venue˜The œcryosphere · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsEnvironment and Climate Change CanadaGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsPermafrostActive layerPlateau (mathematics)Hydraulic conductivityEnvironmental scienceAltitude (triangle)PrecipitationAtmospheric sciencesMonsoonGlobal warmingThermal conductivityGeologySoil scienceClimatologyHydrology (agriculture)Climate changeSoil waterLayer (electronics)Materials scienceMeteorologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract. Seasonally variable thermal conductivity in active layers is one important factor that controls the thermal state of permafrost. The common assumption is that this conductivity is considerably lower in the thawed than in the frozen state, λt/λf < 1. Using a 9-year dataset from the Qinghai–Tibet Plateau (QTP) in conjunction with the GEOtop model, we demonstrate that the ratio λt/λf may approach or even exceed 1. This can happen in thick (> 1.5 m) active layers with strong seasonal total water content changes in the regions with summer-monsoon-dominated precipitation pattern. The conductivity ratio can be further increased by typical soil architectures that may lead to a dry interlayer. The unique pattern of soil hydraulic and thermal dynamics in the active layer can be one important contributor for the rapid permafrost warming at the study site. These findings suggest that, given the increase in air temperature and precipitation, soil hydraulic properties, particularly soil architecture in those thick active layers must be properly taken into account in permafrost models.

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)0.000
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 score0.209
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.027
GPT teacher head0.244
Teacher spread0.217 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

Explore more

Same venue˜The œcryosphereSame topicClimate change and permafrostFrench-language works237,207