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Record W6977159893 · doi:10.6084/m9.figshare.c.7891699

Supplementary material from "Unsettling the record: modelling the devastating cumulative effects of selected environmental stressors and loss of human life caused by colonization in Burrard Inlet, Canada"

2025· other· en· W6977159893 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEcosystemFishingCumulative effectsColonizationPopulationIndigenousPsychological resilienceStressorEcosystem health

Abstract

fetched live from OpenAlex

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 due to industrial and urban pollution. Our results show dramatic change in the ecosystem state from these events, with significant losses in biomass and degradation of ecosystem health during the 230 years 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.000

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.

Opus teacher head0.013
GPT teacher head0.231
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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