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Record W4366594798 · doi:10.1007/s10745-023-00398-w

The Rise of Vancouver and the Collapse of Forage Fish: A Story of Urbanization and the Destruction of an Aquatic Ecosystem on the Salish Sea (1885–1920 CE)

2023· article· en· W4366594798 on OpenAlexaffabout
Jesse Morin, Aaron Blake Evans, Meaghan Efford

Bibliographic record

VenueHuman Ecology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsFisheryGeographyUrbanizationIndigenousFish <Actinopterygii>Forage fishHerringHistorical ecologyEcosystemSmeltSettlement (finance)ForageHabitatEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Since its establishment as a Euro-Canadian settlement in the mid-nineteenth century, the marine ecology surrounding Vancouver in British Columbia, Canada, has been negatively impacted by urban development, habitat destruction, poor fisheries practices, and pollution. Focussing on forage fish – herring, smelt, and eulachon – we present the results of an extensive meta-analysis including an archaeological, ethnohistoric, and scientific/regulatory literature review of Indigenous and commercial fisheries’ harvesting records to track the early historic collapse of these fisheries from about 1885–1920 CE. We identify significant reductions in the major forage fish fisheries around Vancouver within decades of the initial Euro-Canadian settlement. These severe negative effects occurred long before scientific description of local ecosystems had begun, and the magnitude of these effects went generally unrecognized and/or are poorly understood. We argue that this is a case of the shifting baseline syndrome (SBS): each generation of researchers mistakenly assumes that modern ecological conditions they encounter approximate their natural pre-contact state.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.188
Teacher spread0.179 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2023
Admission routes2
Has abstractyes

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