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Record W3045955552 · doi:10.1007/s10750-020-04362-3

Assessment and management of effects of large hydropower projects on aquatic ecosystems in British Columbia, Canada

2020· article· en· W3045955552 on OpenAlexaffabout
Michael J. Bradford

Bibliographic record

VenueHydrobiologia · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsHydropowerHydroelectricityDownstream (manufacturing)River ecosystemAquatic ecosystemEcosystemEnvironmental scienceEnvironmental resource managementEcologyBusinessBiology

Abstract

fetched live from OpenAlex

Abstract Most of the hydropower generated in Canada’s western province of British Columbia is generated by a small number of large storage or diversion projects that impound large rivers. All but one were built between 1950 and 1985; a period when environmental considerations for large projects were evolving to present-day social, political and regulatory standards. Large projects result in ecosystem transformations; river valleys are converted to reservoirs, and the release of water for power generation results in highly altered flow regimes downstream of dams. I describe the effects of three projects on aquatic ecosystems and measures that have been taken over the past 60 years to monitor and mitigate those effects, with an emphasis on downstream effects to fish populations. I briefly review methods that were used to predict effects, particularly on key fish species, and consider the role of adaptive management and its alternatives on resolving uncertainties about ecological effects of large hydroelectric projects.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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