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Record W2768884839

Development of integrated multi-trophic aquaculture in the Bay of Fundy, Canada: A socio-economic case study.

2006· article· en· W2768884839 on OpenAlexaboutno aff
Neil B. Ridler, Bryn Robinson, Thierry Chopin, Shawn Robinson, Fred H. Page

Bibliographic record

VenueWorld aquaculture. · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBayAquacultureBusinessFisherySocioeconomic developmentBeneficiaryNatural resource economicsSocioeconomic statusEnvironmental planningEnvironmental resource managementEconomic growthGeographyEconomicsEcologyPopulationSociologyFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

While there are different definitions of sustainability, there is consensus that the concept includes socioeconomic variables as well as environmental integrity. For aquaculture to be sustainable there must be economic viability. The species cultivated must be profitable and not too risky. Without these two conditions entrepreneurs will have no incentive to invest, or will need permanent subsidies if they are to remain in the industry. In addition, there must be social acceptance of the industry. In a keynote address to the Millennium Conference on aquaculture, it was noted that “technological progress in the next millennium has to go hand-in-hand with social and ethical acceptability” (Pillay 2001). Enough evidence exists from around the world of the deleterious effects on the industry itself when there is social discontent from litigation, theft and vandalism. On the other hand, if there is community support, the aquaculture industry itself can be the primary beneficiary (Katrandis et al. 2003). These socioeconomic determinants complement the further criterion of sustainability; that aquaculture should be environmentally neutral, if not benign. This paper describes a project in the Bay of Fundy in New Brunswick that attempts to enhance aquaculture sustainability. The Bay of Fundy is Canada’s second largest area of salmon farming, after British Columbia. At present, Atlantic salmon are raised in cages using monoculture techniques, amid concerns about environmental and socioeconomic sustainability (Auditor General 2004). However, an alternative to monoculture is an integrated technique that cultivates different species on the same site. The project in the Bay of Fundy has three species: kelp, mussels and salmon at the same site, in the expectation that environmental and socioeconomic risks will be reduced because of synergies among the three species. The implications of this project are not confined to New Brunswick or even salmon farming. In Asia, paddy fields are often used to cultivate both rice and fish. In Ecuador, disease has prompted farmers to integrate shrimp with tilapia. In southern Africa, tilapia cultivation is integrated with cash crops such as hogs, bananas or crocodiles. Diversifying into additional crops reduces risks; it may also reduce feed costs and therefore add to farm profitability. To indicate the economic risks facing the salmon farming industry in the Bay of Fundy, the first section of this article illustrates the explosive growth of the industry worldwide. Aquaculture is the world’s fastest growing source of food and mariculture has grown particularly rapidly since the late 1980s. One mariculture species is Atlantic salmon, which is Canada’s principal cultivated species, by value. It is an internationally traded commodity, so expansion in the major producing countries jeopardizes small-scale producers in Canada. The second section suggests how data support a tentative conclusion that integrated aquaculture is sustainable economically. A third section summarizes a survey of social attitudes toward the project in the region. It answers the question of whether or not the public is in favor of integrated aquaculture, and what their concerns are.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0130.003
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.240
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations32
Published2006
Admission routes1
Has abstractyes

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