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Record W2980362210 · doi:10.1007/s00267-019-01214-2

Quantifying Restoration Offsets at a Nuclear Power Plant in Canada

2019· article· en· W2980362210 on OpenAlexfundaboutno aff
L.W. Barnthouse, Cherie‐Lee Fietsch, David Snider

Bibliographic record

VenueEnvironmental Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersBruce Power
KeywordsOffset (computer science)Environmental scienceHabitatShoreMetric (unit)Nature ConservationEnvironmental resource managementPower stationNuclear powerStructuringHydrology (agriculture)Computer scienceEcologyFisheryOperations managementEngineeringBusiness

Abstract

fetched live from OpenAlex

In Canada, the Fisheries Act requires all water takers to avoid, mitigate and offset fish losses. To satisfy the act's requirements, operators of power plants are required to undertake habitat restoration projects to compensate for fish impinged and entrained at cooling water intake structures. Scaling the quantity of restoration needed, and measuring whether adequate compensation has been achieved, requires a metric that expresses the losses and gains in comparable units. Development of such a metric is especially difficult in the case of power plants, because the losses often consist of a mix of species and life stages that are very different from those produced by technically feasible restoration projects. This paper documents the method that has been developed for quantifying offsets for impingement and entrainment at the Bruce Generating Stations on the eastern shore of Lake Huron, and demonstrates how the method is being used to estimate the offset to be provided by removal of a dam on the nearby Saugeen River.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.997

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.004

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 head0.166
Teacher spread0.160 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2019
Admission routes2
Has abstractyes

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