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Record W1024239577

Estimating ground ice volumes in tundra polygon networks

2005· article· en· W1024239577 on OpenAlexaboutno aff
T. Haltigin, Hugues Lantuit

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsTundraPermafrostArcticSea iceGlobal warmingArctic ice packEnvironmental scienceGeologyArctic vegetationClimate changeClimatologyPhysical geographyGeographyOceanography
DOInot available

Abstract

fetched live from OpenAlex

As outlined in the previous IAF congress in Vancouver, global warming is a majorand growing concern in Arctic regions. Climatic changes in the polar regions of earth are believed to be at least twice as dramatic as in others. A wide international and interconnected approach to the investigation of its impacts is needed. Remote sensing of Arctic regions is unique as it can be used to provide periodic, reliable and precise datasets of any given region. Field research, on the other hand iscrippled by the remoteness, the cost, and the short time frame generally involved. While a considerable amount of research has been conducted on sea ice, sea surface temperatures, and sea currents, few studies have been focusing on the impacts of climate change on theland, and in particular on the permafrost that underlies the entire Arctic. This critical issue has to be addressed rapidly since it directly affects the Inuit communities, the flora, and the fauna of the Arctic.One singular component of the Arctic regions is the presence of tundra polygons, also termed patterned ground. Tundra polygons are linked to the thermal contraction ofthe ground at very low temperatures. They can be found on Earth and on Mars and are observable over large areas, delineating fractal-like networks on the ground. They are characterized by the presence of large quantities of ice at their edges. The imminenceof considerable warming of air temperatures in the Arctic will undoubtedly lead to the melting of most ice, subsequently inducing a lowering of the ground over thousands ofsquare kilometres. No method presently exists to automatically delineate these networks of polygons, and thereafter to quantify the volumes of ice. In this study we present the first attempt to quantify the volumes associated with the thermal contraction fractures and subsequently the volumes of ice present in the ground using high resolution imagery. We investigated several terrains in the western and high Canadian arctic and validated this method with intensive field campaigns. Geophysical methods including ground penetrating radar and capacitive-coupled resistivity were used to provide and in situ mapping of the subsurface, yielding the necessary calibration datasets.Ikonos 1 m imagery for several locations of the Arctic was used to process the algorithm developed at McGill University (Canada) and at the Alfred Wegener Institute (Germany) by the authors. A set of directional edge-enhancement filter combined with radiometric enhancements was applied to the images and yielded remarkably sharp and clear images of the polygonal networks. In addition, geophysical yield data proved to corroborate the patterns extracted by the algorithm. Further processing allowed us to make a first assessment of the volumes of ice associated with the polygons and subsequently of the potential settlement of the ground.The method could be extended to the study of mars polygons (MOC imagery) andsimilarly produced sharp images with clearly defined delineations of the network. This method emphasizes the crucial role played by satellite imagery in the study of climate change environmental impacts in remote Arctic regions. Current innovations including the launch of new high resolution sensors and constellations of such sensors will allow periodic and intensive studies of remote and inaccessible locations. In addition,this technique, by its universal component, can serve current reassessments of potential permafrost hazards in remote Arctic regions.

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)0.003
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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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
Published2005
Admission routes1
Has abstractyes

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