Modeling Coast Salish landscape/seascape use and territory with GIS
Bibliographic record
Abstract
In this paper we use Least Cost Analysis (LCA) and GIS to model site catchments for villages from two Coast Salish tribes—Tsleil-Waututh and Sts'ailes. Here, we use Tobler's Hiker Function to model travel by land and develop a cost raster for travel by water in canoes. This model is then used to describe the site catchments for a number of villages. Comparison of the LCA model with recorded resource use patterns and modern hiking and canoeing times suggest that the model accurately describes traditional landscape and seascape use. The shape and size site catchments emphasize the importance of canoe travel in structuring Coast Salish daily foraging radii (~13 km). The large size site catchments of individual villages indicates that even one centrally-placed village could have exploited much of the tribal territory on a near-daily basis. Further, we find correspondence between our LCA modeled use areas and observed use areas, and the extent of each tribes' respective territory, indicating that the model is accurate in predicting past use areas, and that such use areas closely reflect the metes and bounds of a tribes' territory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".