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Record W2338608720 · doi:10.2166/wp.2015.169

Implementation strategies and a cost/benefit comparison for compliance with an environmental flow regime in a Mediterranean river affected by hydropower

2015· article· en· W2338608720 on OpenAlexaff
Mònica Bardina, Jordi Honey‐Rosés, Antoni Munné

Bibliographic record

VenueWater Policy · 2015
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of British Columbia
FundersEuropean Environment Agency
KeywordsWater Framework DirectiveHydropowerAgency (philosophy)WelfareHydroelectricityBusinessCost–benefit analysisCatalanWater resourcesDirectiveEnvironmental resource managementEnvironmental planningEnvironmental scienceEconomicsWater qualityEngineeringEcologyComputer science

Abstract

fetched live from OpenAlex

Compliance with the Water Framework Directive (WFD) of the European Union requires water managers to establish environmental flow regimes (EFR) in rivers across the continent. Few water agencies have examined the economic and social welfare impacts of implementing environmental flow requirements. We present the approach used by the Catalan Water Agency to calculate an EFR and estimate the economic implications of its implementation in the Ter River (Catalonia, NE Spain), altered by weirs for hydro-electric production. We analyze various implementation strategies and their associated economic costs and benefits, concluding that the restoration of environmental flows in the Ter River has reasonable costs and is likely to be a socially desirable policy with economic benefits exceeding costs. This paper provides an example of how a water agency can generate policy-relevant information on the social welfare impacts of implementing environmental flow policies as mandated by the WFD.

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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
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 head0.028
GPT teacher head0.275
Teacher spread0.246 · 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 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

Citations3
Published2015
Admission routes1
Has abstractyes

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