Terrestrial laser scanning for the documentation of an at-risk buffalo jump (EgPp-26) in south-central Alberta
Bibliographic record
Abstract
This paper reports on the use of ground-based LiDAR (terrestrial laser scanning—TLS) to digitally capture a buffalo jump site located in south-central Alberta (EgPp-26). We discuss how the resulting digital data can be used to create accurate 3D reconstructions and how the application of these high-resolution geospatial datasets can be used for quantifying analyses. Accurate measurements can be taken directly from TLS datasets for use in mapping, as well as 3D visualization of geoarchaeological data. Furthermore, the acquisition of multiple TLS datasets over time can be used to quantify morphometric change and erosional processes impacting archaeological sites. Analytical data from TLS scans can help document often understudied aspects of geoarchaeological processes and facilitate new interpretations at archaeological sites. This technology was rapidly deployed at the Wearmouth Buffalo Jump for the purposes of documentation, monitoring, and digital preservation. The resulting datasets provide a lasting digital record of the site, as it appeared in September of 2016 and 2017. As natural disasters such as flooding and wildfire increase in frequency, we conclude that reality-capture technologies, such as terrestrial laser scanning, are effective tools for monitoring, managing, and preserving heritage resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".