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Initial results of remediation activities to restore hypereutrophic Villerest Reservoir (Roanne, France)

2004· article· en· W1990747070 on OpenAlexaff
Louis‐B. Jugnia, Didier Debroas, J‐C. Romagoux, Jean Dévaux

Bibliographic record

VenueLakes & Reservoirs Science Policy and Management for Sustainable Use · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsPhosphorusEnvironmental remediationTributaryEutrophicationNutrientPhosphateNitrogenWater qualityEnvironmental chemistryChlorophyll aEnvironmental scienceMicrocystis aeruginosaAnimal scienceChemistryHydrology (agriculture)ContaminationEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract We examined the impacts of remediation activities aimed at improving the water quality of hypereutrophic Villerest Reservoir, in which Microcystis aeruginosa dominated during the summer. We also compared nutrients and chlorophyll a data from this study with the results of a previous study on the reservoir. Between the two studies, the nitrogen and phosphorus loads into the reservoir from the main tributary decreased by 70% and 80%, respectively. Within the reservoir, the quantities of ammonia‐nitrogen were similar in the two studies, and the total nitrogen was significantly higher in this study compared to the initial study. Both the phosphate‐phosphorus and total phosphorus concentrations decreased significantly between the two studies. However, the statistically significant decrease in phosphate‐phosphorus and total phosphorus did not always lead to a significant decrease in chlorophyll a concentrations. The nitrogen/phosphorus mass ratio during the present study remained well above five, the critical value below which summer blooms of Microcystis aeruginosa were observed in Villerest Reservoir. These study results indicated that the remediation activities being used to improve the water quality of Villerest Reservoir were off to a good start.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.265
Teacher spread0.253 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations16
Published2004
Admission routes1
Has abstractyes

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