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Record W2126689975 · doi:10.4319/lo.2001.46.3.0623

Fluxes of methylmercury to the water column of a drainage lake: The relative importance of internal and external sources

2001· article· en· W2126689975 on OpenAlexafffund
Patricia Sellers, Carol A. Kelly, John W. M. Rudd

Bibliographic record

VenueLimnology and Oceanography · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsMethylmercuryWater columnEnvironmental scienceHydrology (agriculture)WetlandHypolimnionWater balanceSurface waterSurface runoffDrainageDrainage basinInflowEutrophicationEnvironmental chemistryGeologyEcologyOceanographyChemistryNutrientBioaccumulationEnvironmental engineering

Abstract

fetched live from OpenAlex

We studied fluxes of methylmercury (MeHg) through a Precambrian Shield lake using a mass balance approach. The primary goal of the study was to determine the importance of various sources of MeHg to the water column of the lake. The relative importance of all sources was: in‐lake production >>> inflow from a brown‐water lake with riparian wetlands >>> wet deposition > inflow from an upstream oligotrophic lake > direct inflow from uplands surrounding the lake. MeHg accumulated in the hypolimnion of Lake 240 when oxygen was present. Water‐column sinks for MeHg included photodegradation of MeHg, which was about 3.5 times greater than the loss of MeHg through outflow. At present, there are few studies available on mass balance fluxes of MeHg in lakes, and this is the first study that includes losses of MeHg by photodegradation. The inclusion of photodegradation in this study results in a clear demonstration that in‐lake production of MeHg is very important. In drainage lakes, the relative importance of in‐lake production versus inflow of MeHg from wetlands will vary according to the extent of wetlands in the drainage basin, as well as the volume of precipitation, which produces runoff and transports MeHg from wetlands to downstream lakes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.424

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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 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

Citations118
Published2001
Admission routes2
Has abstractyes

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