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Competing use of marine space in a modernizing fishery: salmon farming meets lobster fishing on the Bay of Fundy

2007· article· en· W1523765686 on OpenAlexaffvenue
Bradley B. Walters

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsMount Allison University
Fundersnot available
KeywordsFisheryFishingAquacultureAgricultureBayContext (archaeology)GeographyBusinessFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

Coastal fishing communities are frequently portrayed as bastions of tradition at odds with the modernizing forces of technological change and industrial capitalism. This article examines this debate in the context of intensive aquaculture introduced into regions formerly dependent on the wild fishery, specifically with respect to the explosive growth of salmon farming in New Brunswick. New farm sites are large, often located within or close to traditional lobster fishing areas, which has motivated considerable opposition from local fishermen. This article presents findings from research on interactions between salmon farming and lobster fishing around Deer Island and Grand Manan, New Brunswick. Fishermen and salmon farmers are concerned about possible long‐term effects of farm operations on marine environmental quality and lobster health, and many are concerned about the concentration of ownership and lack of local control over the aquaculture industry. The potential for physical displacement from traditional fishing grounds is real, but the actual impacts have been tempered by a combination of factors, including unusually large lobster catches in recent years; technological advances that have encouraged a shift in lobster fishing effort further offshore, away from salmon farm sites; and social accommodations between salmon site managers and those who fish

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.007
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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 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

Citations48
Published2007
Admission routes2
Has abstractyes

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