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Record W2259380578

Policy Implications of Warming Permafrost

2012· book· en· W2259380578 on OpenAlexaboutno aff
Kevin Schaefer, Hugues Lantuit, V. E. Romanovsky, Edward A. G. Schuur

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2012
Typebook
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostTerminologyGlobal warmingClimate changeEnvironmental resource managementClimate scienceEarth scienceGeographyEnvironmental sciencePhysical geographyEnvironmental planningEcologyGeology
DOInot available

Abstract

fetched live from OpenAlex

Permafrost is perennially frozen ground occurring in about 24% of the exposed land surface in the Northern Hemisphere.The distribution of permafrost is controlled by air temperature and, to a lesser extent, by snow depth, vegetation, orientation to the sun and soil properties.Any location with annual average air temperatures below freezing can potentially form permafrost. Snow is an effective insulator and modulates the effect of air temperature, resulting in permafrost temperatures up to 6°C higher than the local mean annual air temperature.Most of the current permafrost formed during or since the last ice age and can extend down to depths of more than 700 meters in parts of northern Siberia and Canada.Permafrost includes the contents of the ground before it was frozen, such as bedrock, gravel, silt and organic material.Permafrost often contains large lenses, layers and wedges of pure ice that grow over many years as a result of annual freezing and thawing of the surface soil layer.About 24% of the northern hemisphere land surface contains permafrost, divided into zones of continuous, discontinuous, sporadic and isolated patches of permafrost, depending on how much of the land area contains permafrost.Two global networks monitor permafrost status: the Thermal State of Permafrost (TSP) network measures permafrost temperature at various depths in 860 boreholes, and the Circumpolar Active Layer Monitoring (CALM) network measures the thickness of the active layer at 260 sites.The active layer thickness is the maximum surface thaw depth in summer.The TSP and CALM networks are the two components of the Global Terrestrial Network for Permafrost (GTN-P), under the auspices of the Global Climate Observing System (GCOS).The International Permafrost Association (IPA) currently coordinates international development and operation of the TSP and CALM networks for the GTN-P.TSP observations indicate that permafrost temperatures have risen over the past few decades.CALM observations are less conclusive due to the melting of ice layers and lenses in near surface permafrost, but show increases in active layer thickness at many sites.Overall, these observations indicate that large-scale thawing of permafrost may have already started.Arctic and alpine air temperatures are expected to increase at roughly twice the global rate and climate projections indicate substantial loss of permafrost by 2100.A global temperature increase of 3°C means a 6°C increase in the Arctic, resulting in anywhere between 30 to 85% loss of near-surface permafrost.Such widespread permafrost degradation will permanently change local hydrology, increasing the frequency of fire and erosion disturbances.The number of wetlands and lakes will increase in continuous permafrost zones and decrease in discontinuous zones, but will decrease overall as the continuous permafrost zone shrinks, impacting critical habitat, particularly for migratory birds.Risks associated with rock fall and erosion will increase, particularly in cold mountain areas.Damage to critical infrastructure, such as buildings and roads, will incur significant social and economic costs.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0290.002

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 head0.036
GPT teacher head0.285
Teacher spread0.249 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations49
Published2012
Admission routes1
Has abstractyes

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