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Record W2951253163 · doi:10.1029/2019gl082187

Climate Change Drives Widespread and Rapid Thermokarst Development in Very Cold Permafrost in the Canadian High Arctic

2019· article· en· W2951253163 on OpenAlexaffabout
Louise Farquharson, V. E. Romanovsky, William Cable, Donald A. Walker, Steven V. Kokelj, Dmitry Nicolsky

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGovernment of Northwest Territories
FundersNational Science Foundation of Sri Lanka
KeywordsThermokarstPermafrostArcticEnvironmental scienceClimate changePhysical geographyVegetation (pathology)ClimatologyGlobal warmingGeologyAtmospheric sciencesOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Climate warming in regions of ice‐rich permafrost can result in widespread thermokarst development, which reconfigures the landscape and damages infrastructure. We present multisite time series observations which couple ground temperature measurements with thermokarst development in a region of very cold permafrost. In the Canadian High Arctic between 2003 and 2016, a series of anomalously warm summers caused mean thawing indices to be 150–240% above the 1979–2000 normal resulting in up to 90 cm of subsidence over the 12‐year observation period. Our data illustrate that despite low mean annual ground temperatures, very cold permafrost (<−10 °C) with massive ground ice close to the surface is highly vulnerable to rapid permafrost degradation and thermokarst development. We suggest that this is due to little thermal buffering from soil organic layers and near‐surface vegetation, and the presence of near‐surface ground ice. Observed maximum thaw depths at our sites are already exceeding those projected to occur by 2090 under representative concentration pathway version 4.5.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.275
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations321
Published2019
Admission routes2
Has abstractyes

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