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Record W2418388587 · doi:10.1007/s00267-016-0716-0

Non-use Economic Values for Little-Known Aquatic Species at Risk: Comparing Choice Experiment Results from Surveys Focused on Species, Guilds, and Ecosystems

2016· article· en· W2418388587 on OpenAlexafffundabout
Murray A. Rudd, Sheri Andres, Mary A. Kilfoil

Bibliographic record

VenueEnvironmental Management · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsDalhousie UniversityFisheries and Oceans Canada
FundersFisheries and Oceans CanadaGovernment of Canada
KeywordsGuildWillingness to payWetlandEcosystemAquatic ecosystemEcosystem servicesEcologyBiodiversityListing (finance)GeographyEnvironmental resource managementBiologyEnvironmental scienceBusinessHabitatEconomics

Abstract

fetched live from OpenAlex

Accounting for non-market economic values of biological diversity is important to fully assess the benefits of environmental policies and regulations. This study used three choice experiments (species-, guild-, and ecosystem-based surveys) in parallel to quantify non-use values for little-known aquatic species at risk in southern Ontario. Mean willingness-to-pay (WTP) ranged from $9.45 to $21.41 per listing status increment under Canada's Species at Risk Act for both named and unnamed little-known species. Given the broad range of valuable ecosystem services likely to accrue to residents from substantial increases in water quality and the rehabilitation of coastal wetlands, the difference in WTP between species- and ecosystem-based surveys seemed implausibly small. It appeared that naming species-the 'iconization' of species in two of the three surveys-had an important effect on WTP. The results suggest that reasonable annual household-level WTP values for little-known aquatic species may be $10 to $25 per species or $10 to $20 per listing status increment. The results highlighted the utility of using parallel surveys to triangulate on non-use economic values for little-known species at risk.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.079
GPT teacher head0.209
Teacher spread0.130 · 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

Citations23
Published2016
Admission routes3
Has abstractyes

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