The Impact of Vegetation on Archaeological Sites in the Low Arctic in Light of Climate Change
Bibliographic record
Abstract
Vegetation is changing across the Arctic in response to increasing temperatures, which may influence archaeological sites in the region. At the moment, very little is known about how different plant species influence archaeological remains. In this study we visited 14 archaeological sites stretching across a climatic gradient from the outer coast to the inner fjords in the Nuuk Fjord area of West Greenland to assess the impact of vegetation growth on archaeological preservation. Examination of the physical disturbance of archaeological layers and materials by roots from different plant species showed that horsetail (Equisetum arvense) was particularly destructive because of its deep penetrating rhizomes and ubiquity across the study area. Willow (Salix glauca) also caused physical disturbance due to a dense root network, but its roots were mainly found in the upper 30 cm of the soil. Focus was also given to the impact from vegetation on the visibility of sites, where growth of willow was found be the main problem, especially in the inner fjords. Historic descriptions and aerial photographs from the sites show that shrub growth was already widespread in the region by the 1930s, but photos of some of the sites investigated show that the willow shrubs are significantly taller today, which has decreased the visibility of site features. The impact from horsetail and willow on archaeological sites may be mitigated using geotextiles and grazing by livestock, but both methods require further studies before being implemented in the study area.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".