Bibliographic record
Abstract
Between 1988 and 1989, an intensive archaeological survey of a small drainage known as Roan Mare coulee in southern Saskatchewan was conducted by Dr. Ernest Walker (Walker 1990). Among the 120 archaeological sites in the area, seven bison kills and a vast array of associated drivelines were identified. This study focuses upon the spatial interaction amongst the kills, the drivelines and the local environment in relation to the bison hunting strategies used on the Northern Plains. This is done by modelling where bison are likely to move in the terrain as well as how the topography obstructs their line of sight. As this problem covers a large spatial area and multiple different data sources, Geographic Information Systems (GIS) are integrated into the research design in the form of Least Cost Path and Viewshed analyses. Both archaeological data from Walker's survey and environmental data such as elevation and water sources served as the input datasets required by ArcGIS's spatial analysis tools. The results of the Least Cost Path analyses were compared visually to both the location and orientation of the driveline evidence, while the viewshed results were compared to the trap's location at the valley edge. The results of this research showed that the drivelines found at Roan Mare coulee appear to be following the general orientation of the landscape at the broadest scales, and likely served to funnel bison over large distances. There also appear to be several locations on the landscape that are amenable to moving bison to several different sites. The viewshed evidence shows the smaller scale nuances between bison vision and the terrain in a hypothetical drive event. The differences in the viewable area available to the bison at each site likely played a role in the chosen strategy employed when that site was used. It is hoped that this style of research can be continued with higher quality data and additional variables to help clarify many of the subtleties found in a Plains bison drive.
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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 | 0.003 | 0.011 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".