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Record W4392584910 · doi:10.5194/egusphere-egu24-6955

Significant subglacial and proglacial lake drainages in the Canadian Arctic identified by time-stamped ArcticDEM strips

2024· preprint· en· W4392584910 on OpenAlexaffabout
Whyjay Zheng, Wesley Van Wychen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsArcticSTRIPSGeologyThe arcticHydrology (agriculture)GeomorphologyOceanographyArchaeologyGeographyGeotechnical engineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Active subglacial and proglacial lakes drain and fill constantly, contributing to glacier and ice sheet mass balance in a way that is not always easy to quantify. Active subglacial lakes account for about 20% of the first worldwide subglacial lake inventory (published by Livingstone et al., 2022); however, none have been previously identified in the Canadian Arctic. Here, we report at least 28 drainage and fill events identified by analyzing time-stamped ArcticDEM elevation data. We stack all the available 2-m DEM strips (23,691 in total) from the latest ArcticDEM release (October 2022) and calculate the elevation change rate at every 15-m sized pixel in a reference grid. Glacier areas with the following signals are interpreted to be associated with the lake drainage or refill beneath the ice: (1) a significantly higher elevation change rate than the neighboring regions within the same glacier catchment, and (2) no adjacent zones showing reversed elevation change (to avoid surge event being misclassified). If such an area touches the glacier terminus, we interpret the elevation change to be governed by a proglacial lake where the floating ice terminus rises and falls when the lake level changes. These drainage and refill events are scattered throughout the region, from the North Ellesmere Icefields to Penny Ice Cap (South Baffin Island). Almost none of the lake locations have been previously reported, probably due to their small size (a few kilometers wide on average), but some of them caused significant ice elevation drops of up to 100 meters during a drainage event. It is not clear whether these significant drainage events produced outburst floods due to temporal sampling gaps in the data. Nevertheless, the water mass lost or gained during the events should be independently calculated from the land ice budget, and we should keep monitoring these newly discovered lakes for their potential impact on the ice flow dynamics and localized mass balances, especially in the context of rapid Arctic warming.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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
Published2024
Admission routes2
Has abstractyes

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