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Record W1979988430 · doi:10.1139/f05-103

Methyl mercury concentrations in macroinvertebrates and fish from burned and undisturbed lakes on the Boreal Plain

2005· article· en· W1979988430 on OpenAlexfundvenueaboutno aff
Erik W. Allen, Ellie E. Prepas, Stephan Gabos, William M. J. Strachan, Weiping Zhang

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHealth Canada
KeywordsBiotaEnvironmental scienceMethylmercuryEnvironmental chemistryMercury (programming language)Trophic levelBioaccumulationAquatic ecosystemBiomagnificationFood webEcologyBorealHydrology (agriculture)ChemistryBiologyGeology

Abstract

fetched live from OpenAlex

Methyl mercury (MeHg) concentrations in macroinvertebrates and fish were compared among five lakes in burned catchments and five reference lakes on the western Canadian Boreal Plain to determine the influence of forest fire on MeHg bioaccumulation. Two years after fire, MeHg concentrations in five of six aquatic taxa were similar in burned and reference lakes. Among a larger set of 12 lakes, MeHg concentrations in biota were negatively correlated with lake water pH, trophic status, and hardness, reflecting a pre-existing gradient in water chemistry. Biomagnification of MeHg (as determined by regression of MeHg concentration on baseline-adjusted δ 15 N) was negatively correlated with lake water chlorophyll a concentration. A subsequent logging experiment interrupted by fire provided an opportunity to compare pre- and post-fire MeHg concentrations in aquatic biota. Three months after fire, biota MeHg concentrations had decreased by 32%–50%, and lake water ammonium concentrations had increased 11-fold relative to the previous year. In this nutrient-rich setting, fire may lower MeHg concentrations in aquatic biota over the short-term by inducing an increase in lake productivity that dilutes MeHg at the base of the food web.

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 score0.230
Threshold uncertainty score0.994

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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

Citations54
Published2005
Admission routes3
Has abstractyes

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