The Application of Terrestrial Laser Scanning for the Documentation and Monitoring of a Threatened Buffalo Jump Heritage Site in South-central Alberta
Bibliographic record
Abstract
The research presented outlines the use of terrestrial laser scanning as a method of digitally capturing an at-risk Buffalo Jump site in south-central Alberta. The bison faunal material and other archaeological remains present at the base of the sandstone cliff were exposed following the 2013 flooding and migration of the main channel of the Jumping Pound Creek, and the remnant portion of the site is classified as highly threatened due to natural erosional processes along the riverbank. Terrestrial LiDAR was implemented to digitally capture the site and surrounding landscape in the fall of 2016, and a second point cloud dataset was collected in the fall of 2017. Results of the cloud-to-cloud comparison of the two non-contemporaneously collected datasets determined that in certain areas along the cutbank of the site locality over 1.5 meters of erosion took place. Measurements of the sedimentary cutbank and geologic profiling were recovered from the 3-dimensional spatial data collected and comparisons were made for determinations of cutbank undercutting and instability at different locations based on the point-cloud datasets. This research will allow for more strategic mitigative archaeological initiatives to be implemented at this site of significant traditional provenance for the Blackfoot peoples to aid in its protection and preservation. TLS implementation creates accurate 3D visualization of archaeological sites it is argued that the analytical possibilities of point clouds and other forms of digital data require further exploration for potential applications in monitoring and use of these datasets for public outreach and sharing of cultural heritage resources. The resulting datasets provide a lasting digital record of the site, at multiple points in time, and the importance of properly archiving datasets for sharing and data compatibility for future use in monitoring is a necessity. As natural disasters such as flooding and wildfire increase in frequency, it is concluded that reality-capture technologies, such as TLS, are effective tools for sharing, documenting, and monitoring heritage resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".