Evolving ice fraction in the shallow firn layer of Devon Ice Cap, Canada, between 2012 and 2022
Bibliographic record
Abstract
Abstract Firn can store glacial meltwater and delay contribution to sea level rise, but ice layers and ice slabs within the shallow firn layer can impede the downward percolation of melt. Here we report firn conditions along a transect on southwest Devon Ice Cap (DIC), Nunavut, and explore its response to air temperature variability over a decadal period. We present results from two field campaigns, during which six shallow firn cores were extracted along the same transect in spring 2012 and 2022. At all sites, the ice fraction (IF) was less in 2022 than in 2012, and the firn content increased. Between 2012 and 2022, the IF of the firn layer changed by −30% at the lowest elevation site (1400 m a.s.l.) and by −11% at the highest elevation site (1800 m a.s.l.) and by an average of −26% across all sites. Despite higher annual positive degree day sums during 2012–22 compared to 2002–12, cooler summers in 2013, 2018 and 2021 resulted in less ice content in the shallow firn layer. This demonstrates that the shallow firn layer can regenerate from several cooler years and highlights the nuanced response of the DIC shallow firn layer to climate 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".