Assessing recent thaw and subsidence of peatland permafrost in coastal Labrador, northeastern Canada
Bibliographic record
Abstract
Ground temperatures have been monitored since 2014 in four shallow boreholes (up to 5.7 m deep) drilled in palsas along the southeastern Labrador Sea coastline. This borehole network is critical for monitoring the effects of climate change on the terrestrial cryosphere and includes some of the southernmost coastal permafrost in the Northern Hemisphere. In this region, there are very few published measurements of active layer thickness, permafrost thickness, and permafrost temperatures. Mean annual ground temperatures of -1.7 to -0.7°C at 1 m depth were surprisingly low at the beginning of the study period, given the relatively thin bodies of permafrost present (<3 m thick). Statistically significant increases in ground temperatures were observed from 2015 to 2022 at some but not all depths in the four boreholes, despite decreases in permafrost thickness observed at all sites. Permafrost thaw resulted from both increased thaw penetration and thaw from the base of permafrost. Thaw penetration relative to the original ground surface increased by 24 to 92% due to a combination of active layer thickening and ground subsidence. Permafrost thaw at these sensitive locations may be driven by changes in mean annual air temperature, vegetation, snow dynamics, hydrology, and human disturbance. These data provide novel insights into the sensitivity of permafrost in this understudied region and can be used to validate predictive thermal modelling under future climate scenarios.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".