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Record W2768064800 · doi:10.1594/pangaea.834904

Characteristics and changes of thermokarst lakes in Canada, Russia and Sweden

2011· dataset· en· W2768064800 on OpenAlexaboutno aff
A. Britta K. Sannel, Peter Kuhry

Bibliographic record

VenueFigshare · 2011
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsThermokarstPhysical geographyEnvironmental scienceGeographyArchaeologyHydrology (agriculture)GeologyOceanographyPermafrostGeotechnical engineering

Abstract

fetched live from OpenAlex

Peat plateaus are widespread at high northern latitudes and are important soil organic carbon reservoirs. A warming climate can cause either increased ground subsidence (thermokarst) resulting in lake formation or increased drainage as the permafrost thaws. A better understanding of spatiotemporal variations in these landforms in relation to climate change is important for predicting the future thawing permafrost carbon feedback. In this study, dynamics in thermokarst lake extent during the last 35-50 years has been quantified through time series analysis of aerial photographs and high-resolution satellite images (IKONOS/QuickBird) in three peat plateau complexes, spread out across the northern circumpolar region along a climatic and permafrost gradient. From the mid-1970s until the mid-2000s there has been an increase in mean annual air temperature, winter precipitation, and ground temperature in all three study areas. The two peat plateaus located in the continuous and discontinuous permafrost zones, respectively, where mean annual air temperatures are below -5°C and ground temperatures are -2°C or colder, have experienced small changes in thermokarst lake extent. In the peat plateau located in the sporadic permafrost zone where the mean annual air temperature is around -3°C, and the ground temperature is close to 0°C, lake drainage and infilling with fen vegetation has been extensive and many new thermokarst lakes have formed. In a future progressively warmer and wetter climate permafrost degradation can cause significant impacts on landscape composition and greenhouse gas exchange also in areas with extensive peat plateaus, which presently still experience stable permafrost conditions.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.433
Threshold uncertainty score0.695

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)0.3050.000

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 head0.223
Teacher spread0.161 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2011
Admission routes1
Has abstractyes

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