Archaeological Site Vulnerability Modelling: The Influence of High Impact Storm Events on Models of Shoreline Erosion in the Western Canadian Arctic
Bibliographic record
Abstract
Abstract Much of the Inuvialuit archaeological record is situated along shorelines of the western Canadian Arctic. These coastal sites are at substantial risk of damage due to a number of geomorphological processes at work in the region. The identification of threatened heritage remains is critical in the Mackenzie Delta, where landscape changes are taking place at an increasingly rapid pace. This paper outlines some preliminary observations from a research program directed toward identifying vulnerable archaeological remains within the Inuvialuit Settlement Region. Coastal erosion rates have been calculated for over 280 km of the Kugmallit Bay shoreline, extending along the eastern extent of Richards Island and neighbouring areas of the Tuktoyaktuk Peninsula. Helicopter surveys conducted during the 2014 field season confirmed that areas exposed to heavy erosive forces in the past continue to erode at alarming rates. Some of the calculated rates, however, have proven far too conservative. An extreme period of erosion at Toker Point in the autumn of 2013 has yielded a prime example of how increasingly volatile weather patterns can influence shoreline erosion models. It has also provided a case with which to demonstrate the value of using more recent, shorter time-interval imagery in assessing impacts to cultural landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".