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Record W3210366094 · doi:10.4236/gep.2021.910005

The Socio-Economic Impacts of Aged-Dam Removal: A Review

2021· review· en· W3210366094 on OpenAlexafffund
Duminda Perera, Taylor Alden North

Bibliographic record

VenueJournal of Geoscience and Environment Protection · 2021
Typereview
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcMaster UniversityUnited Nations University Institute for Water, Environment, and HealthUniversity of Ottawa
FundersGlobal Affairs Canada
KeywordsRecreationEconomic impact analysisEnvironmental planningValue (mathematics)Cultural heritageBusinessDam removalNatural resource economicsEnvironmental resource managementGeographyEnvironmental scienceCivil engineeringEngineeringPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Water storage dams worldwide are ageing, and many will reach the end of their designed lifespan by the middle of the 21st century. Some of these dams will likely need to be removed. While dam construction impacts have been widely discussed, dam removal impacts on society and the economy need to be synthesized and considered in the ageing dams’ decision-making process. This paper summarizes dam removal impacts on the local economy and industry, culture, history and heritage, property value, recreation, aesthetics, and disaster avoidance from identified studies worldwide. It demonstrates that these impacts may vary depending on geography and between developed and developing countries. It concludes that dam removal should consider the cost, environmental, and the socio-economic impacts while including all stakeholders who could be positively and negatively impacted by dam removal.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.236
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2021
Admission routes2
Has abstractyes

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