Unsettling the record: modelling the devastating cumulative effects of selected environmental stressors and loss of human life caused by colonization in Burrard Inlet, Canada
Bibliographic record
Abstract
In this paper we present a collaborative, transdisciplinary research project that explores the cumulative ecological and human impacts of colonization on the səl̓ilwət (Tsleil-Wat, Burrard Inlet) ecosystem in what is now known as British Columbia, Canada. səl̓ilwət is at the heart of the traditional and unceded territory of səl̓ilwətaɬ (Tsleil-Waututh), a Coast Salish Indigenous Nation. This research is conducted at the request and under the leadership of səl̓ilwətaɬ. Drawing on archaeology, historical ecology, historical/archival records, and səl̓ilwətaɬ science, we use Ecopath with Ecosim to model selected environmental stressors and the devastating loss of səl̓ilwətaɬ life caused by colonization, from 1750-1980 CE. We include European-introduced smallpox epidemics, the rise in the settler population and settler fishing pressure, the loss of shoreline habitat and the closure of bivalve harvesting owing to industrial and urban pollution. Our results show dramatic change in the ecosystem state following these events, with significant losses in biomass and degradation of ecosystem health during the 230 years that we assess. We demonstrate the ecological impact that smallpox had through loss of both human life and Indigenous stewardship. This research sits within the palaeoenvironmental, palaeoecological and environmental archaeological space of reconstructing past environments and human-to-environment relationships over deep time.This article is part of the theme issue 'Shifting seas: understanding deep-time human impacts on marine ecosystems'.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".