Conceptual Model of Permafrost Degradation in an Inuit Archaeological Context (Dog Island, Labrador): A Geophysical Approach
Bibliographic record
Abstract
Characterizing permafrost is crucial for understanding the fate of arctic and subarctic archaeological archives under climate change. The loss of bio-physical integrity of archaeological sites in northern regions is still poorly documented, even though discontinuous permafrost is particularly vulnerable to global warming. In this study, we documented the spatial distribution of the permafrost-supported Inuit archaeological site Oakes Bay 1 on Dog Island (Labrador, Canada) while employing a novel approach in northern geoarchaeology based on non-invasive geophysical methods. ERT and GPR were successfully used to estimate active layer thickness and image permafrost spatial variability and characteristics. The results made it possible to reconstruct a conceptual model of the current geocryological context of the subsurface in relation to the site topography, hydrology, and geomorphology. The peripherical walls of Inuit semi-subterranean sod houses were found to contain ice-rich permafrost, whereas their central depressions were identified as sources of vertical permafrost degradation. The geophysical investigations were used to classify the permafrost at Oakes Bay 1 as climate-driven, ecosystem-protected permafrost that cannot regenerate under current climate conditions. This work highlights how the permafrost at Oakes Bay 1 is currently affected by multi-point thermal degradation by both conduction and advection, which makes it highly sensitive 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".