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Record W2284622711 · doi:10.1080/13657305.2016.1124935

A contingent valuation of the biomitigation benefits of integrated multi-trophic aquaculture in Canada

2016· article· en· W2284622711 on OpenAlexafffundabout
Roberto Martı́nez-Espiñeira, T. Chopin, Shawn Robinson, Anthony A. Noce, Duncan Knowler, Winnie Yip

Bibliographic record

VenueAquaculture Economics & Management · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsFisheries and Oceans CanadaSimon Fraser UniversityUniversity of New BrunswickMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsAquacultureContingent valuationFisheryTrophic levelAgricultureValuation (finance)PaymentBusinessEcosystemNatural resource economicsEcologyEconomicsBiologyWillingness to payFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Integrated multi-trophic aquaculture (IMTA) is the farming, in proximity, of aquaculture species from different trophic levels and with complementary ecosystem functions. IMTA allows one species’ uneaten feed and wastes, nutrients, and by-products to be recaptured and converted into fertilizer, feed, and energy for the other crops. By taking advantage of synergistic interactions between species, IMTA can help aquaculture evolve towards more responsible and sustainable systems. This study uses data from a contingent valuation survey to provide an estimation of the non-use benefits that, in the form of biomitigation of the external costs imposed on the marine environment, would be derived by Canadians from the adoption of IMTA for Atlantic salmon aquaculture. We find the benefits accruing to households who do not purchase salmon habitually would range between about $43 million/year and about $65 million/year for the next five years, depending on the treatment of ‘don’t know’ responses to the payment question.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.898

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)0.0000.000

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 head0.051
GPT teacher head0.189
Teacher spread0.138 · 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

Citations19
Published2016
Admission routes3
Has abstractyes

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