<i>Wil Luunda ‘Waada aks</i> (Where the Waters Meet): Deep-time histories of shifting estuarine landscapes and human settlement in <i>Laxgalts’ap</i> watershed, northern British Columbia, Canada
Bibliographic record
Abstract
The geomorphological dynamism of estuarine landscapes provides challenges to archaeologists trying to understand the long-term, intertwined histories of people and place in these ecotones. This is especially the case in regions like the Northwest Coast of North America, where the location of the interface between river and coast has shifted in tandem with changing relative sea level (RSL) positions in the late Pleistocene through the Holocene. We explore the deep-time landscape and settlement histories of the estuarine landscapes of Laxgalts’ap, a culturally significant place for the Gitga’at First Nation on the northern coast of British Columbia, Canada, by melding archaeological, paleoenvironmental, and oral historical knowledge. Using geological data and GIS modeling, we document the evolution of the estuary at Laxgalts’ap in the context of a transformation from a marine channel to an inlet and then a river valley over the last 14,500 years. Our archaeological surveys revealed evidence of human occupation that is over 10,600 years old and spans the Holocene, demonstrating the long-term connections to estuarine areas that are also reflected in Gitga’at oral traditions. This study demonstrates how, with careful attention to geomorphological processes and landscape change, we can document deep-time cultural connections to place, even in the most dynamic landscapes.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".