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Record W1304485

Sensitivity of Permafrost in the Arctic - a multiscale perspective

2011· article· en· W1304485 on OpenAlexaboutno aff
Julia Boike, Moritz Langer, Anna Abnizova, Katrin Fröb, Maren Grüber, Sina Muster, Konstanze Piel, Karoline Wischnewski, Sebastian Westermann, Kurt Roth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostArcticEnvironmental scienceAlbedo (alchemy)Climate changeWater balanceLand coverArctic vegetationEnergy balanceClimate modelClimatologyAtmospheric sciencesPhysical geographyLand useGeologyEcologyTundraOceanographyGeography
DOInot available

Abstract

fetched live from OpenAlex

Permafrost regions occupy approximately 24 % of the Northern Hemisphere’s land area; these regions are anticipated to be considerably reduced by climate change. Comprehensive data sets are sparse for the Arctic, yet they are of great value to support modeling efforts on current and future arctic climate and permafrost conditions. The SPARC (Sensitivity of Permafrost in the ARCtic) research group concentrates on examining heat, water and carbon fluxes in the Arctic permafrost system at sites in Siberia, Svalbard and the Canadian Arctic and how these processes vary across multiple spatial and temporal scales. Specifically, our goals are to: (i) quantify water and energy fluxes across a spectrum of scales, (ii) identify environmental factors and processes controlling the fluxes, and (iii) understand the interactions with biochemical processes determing the carbon balance of large Arctic areas. This poster summarizes the recent results of the following topics: land cover characteristics, surface temperature and energy balance. The surface energy budget is the key to process understanding in permafrost areas, since it determines the surface temperature and thus the seasonal thawing of the soil. The land surface temperature is related to all components of the energy balance and is thus a crucial parameter when monitoring the energy budget of permafrost environments. Land cover affects the biogeophysical properties of the surface like surface hydrology, albedo, and biomass which determine the exchange of energy, water and carbon fluxes between the surface and the atmosphere. Our results show that especially small ponds and lakes play a dominant role in the water and energy budget of Arctic permafrost landscapes. This is of particular importance, as such land cover heterogeneities are usually not accounted for in large-scale climate models. Hence, model derived estimats of surface temperature, ground heat flux, evaporation as well as carbon fluxes might be biased.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.062
GPT teacher head0.250
Teacher spread0.188 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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