An Application of GIS Techniques to Assess the Risk of Disturbance of Archaeological Sites by Mass Movement and Marine Flooding in Auyuittuq National Park Reserve, Nunavut
Bibliographic record
Abstract
Coastal regions within Auyuittuq National Park Reserve (ANPR) are sensitive to mass movement processes and threatened by flooding in response to sea level rise. These processes pose a risk to culturally significant archaeological sites within ANPR. Sites at risk of disturbance need to be identified and protected to conserve valuable archaeological resources. Since the costs of identifying and monitoring sites at risk in remote areas are substantial, modern technologies such as Geographic Information Systems (GIS) can be used to create a more rapid and cost-effective means to monitor coastal environments and manage coastal resources. This study examines the application of GIS technology to assess the risk of disturbance of 44 coastal archaeological sites by mass movement and marine flooding within ANPR. Data on surficial materials and slope angles are combined in an overlay analysis to assess terrain sensitivity to mass movement. The output from this analysis is a coarse regional assessment of mass movement potential as it relates to the strength of materials on slopes. The overall risk of disturbance for archaeological sites within ANPR is assessed by combining the risk of mass movement and the risk of marine flooding. Twenty-eight sites within ANPR are identified as being at considerable risk to disturbance: these sites are located largely on glaciomarine sediments at moderate or high slope angles and are at substantial risk to flooding (less than two metres above sea level).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".