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Record W4214899514 · doi:10.22215/etd/2014-10519

Impact of Lake Expansion on Mercury Concentrations in Lake Sediments, Mackenzie Bison Sanctuary, Northwest Territories, Canada

2014· dissertation· en· W4214899514 on OpenAlexaffabout
Joelle T. Perreault

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsMercury (programming language)Organic matterEnvironmental scienceSedimentEcosystemHydrology (agriculture)Surface waterLake ecosystemWetlandWildlifeMethylmercuryOceanographyPhysical geographyEnvironmental chemistryGeologyEcologyGeographyGeomorphologyBioaccumulationChemistryEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

Spatial and temporal variations in total mercury, organic matter and lake surface area were assessed to determine if flooding in the Mackenzie Bison Sanctuary was influencing Hg inputs to lake sediments. Mercury concentrations in the sediment of lakes examined are below established guidelines. All lakes demonstrated increased Hg concentration and flux over the past century. Two expanding lakes exhibited maximum total Hg values in surface sediments which correlated with peaks in water surface area and changes in source of organic matter. Reference lakes demonstrated declining total Hg values in recent sediments and no correlation with organic matter or water surface area. This study presents land users and managers with a preliminary assessment of Hg concentrations within MBS lakes. Recommended future work should focus on methyl mercury concentrations and methylation rates in sediments, which often increase after landscapes are flooded, and can pose risks to wildlife species relying on the ecosystem.

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)0.000
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 score0.013
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
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 head0.010
GPT teacher head0.265
Teacher spread0.255 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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