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Thermokarst lake expansion and carbon mobilization in polygonal tundra of Old Crow Flats, northern Yukon

2018· other· en· W6920855541 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThermokarstPermafrostTundraPeatArcticShoreHydrology (agriculture)Total organic carbon

Abstract

fetched live from OpenAlex

This poster was presented at the 2018 ArcticNet Scientific Meeting, in Ottawa, ON, held from Dec 10th to 14th, 2018.http://www.arcticnetmeetings.ca/asm2018/index.php Reference: Roy-Levillee P, Burn CR. (2018) Thermokarst lake expansion and carbon mobilization in polygonal tundra of Old Crow Flats, northern Yukon. In ArcticNet Scientific Meeting 2018, Ottawa, ON, December 10th to 14th 2018. doi: 10.6084/m9.figshare.7476098 Abstract: The expansion and drainage of thermokarst lakes respond to climatic trends and are an important part of the permafrost carbon feedback. The effects of climate on total lake area in thermokarst lowlands vary from region to region and may be monitored via remote-sensing, but it is difficult to interpret changes in lake area in terms of volumes of permafrost thawed and organic carbon released from permafrost. This is because the rates of subaerial and sublacustrine permafrost degradation associated with lake expansion, as well as the distribution of organics in the sediment profile, vary across Arctic lowlands due differences in environmental conditions. This research links changes in lake area with tridimensional estimates of permafrost degradation, and assesses associated changes in permafrost carbon storage at the landscape scale. The study area was a zone of polygonal tundra within Old Crow Flats (OCF), YT, a 5600 km2 Arctic peatland located in an inland basin separated from the Arctic Coast by mountains. The research objectives are: 1) to use remotely sensed imagery to assess rates of lake expansion and characterize the relation between lake size and shore erosion rates in the study area; 2) to use modelling in combination with ground temperature measurements and observations of talik geometry to estimate volumetric rates of permafrost degradation beneath expanding lakes; 3) to use field measurements of shore bank height and samples of permafrost to estimate organic carbon content where permafrost degradation is imminent. Results indicate that, between 1951 and 2011, lake expansion encroached on the surrounding tundra at an average rate of 0.27 km2 a-1. The total lake expansion during this period is approximately equal to the total lake area lost to catastrophic drainages in the 1060 km2 study area. Permafrost thaw occurred beneath the areas that became part of the lakes, and this loss of permafrost was compensated by the aggradation of permafrost in drained basins. However, lake expansion occurred via the erosion of organic-rich permafrost banks varying in height between 0.5 and 4 m, representing an additional loss of permafrost. Due to bank erosion alone, approximately 430 000 m3 a-1 of sediment fell in the lakes of the study area annually. This represents an input of organic carbon into the lakes of 0.22 Tg C a-1, of which 0.15 Tg C a-1 was stored in permafrost prior to being thawed during bank erosion. Comparatively, a doubling of active layer depth over the entire study area in the next 20 years would lead to the thawing of 0.01 Tg C a-1 of organic carbon previously stored in permafrost, less than one tenth of what would be released via bank erosion if current erosion rates are sustained. While current climate models with carbon budgets focus on active layer deepening as the main mechanism of permafrost degradation associated with carbon mobilisation, these research results highlight the importance of considering thermokarst lake expansion as a tridimensional process when quantifying the permafrost carbon feedback in Arctic lowlands.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.932

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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