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Record W2141162612 · doi:10.4319/lo.2007.52.6.2629

Effects of impoundment on nutrient availability and productivity in lakes

2007· article· en· W2141162612 on OpenAlexafffundabout
Andreas Matzinger, Roger Pieters, Ken I. Ashley, Gregory A. Lawrence, Alfred Wüest

Bibliographic record

VenueLimnology and Oceanography · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistry of EnvironmentUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of EnvironmentBC Hydro
KeywordsProductivityEnvironmental scienceNutrientPrimary productivityHydrology (agriculture)OncorhynchusOutflowEcologyFish <Actinopterygii>FisheryGeologyOceanographyBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

We investigate the hydraulic alteration and the effect on primary productivity of the Hugh Keenleyside Dam built at the outlet of the Arrow Lakes (British Columbia, Canada) in 1967. Three dam‐induced hydraulic changes have been identified as relevant: (1) water level increase, (2) leveling of the seasonal outflow, and (3) subsurface release of water from the dam. The potential effect of these alterations on primary productivity were tested with a numerical model supported by field observations. Hydraulic modifications can reduce lake productivity by up to 40%, primarily as a result of altered flow path and allowing nutrients to pass through the reservoir without entering the productive zone near the surface. This productivity loss is comparable to the reduction caused by nutrient retention behind dams constructed upstream of the Arrow Lakes Reservoir. The combined productivity loss from both of these two mechanisms is significant and may well be responsible for the dramatic decline of kokanee (Oncorhynchus nerka) observed in the Arrow Lakes Reservoir in the 1990s.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.003
GPT teacher head0.193
Teacher spread0.189 · 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

Citations47
Published2007
Admission routes3
Has abstractyes

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