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Record W2086159358 · doi:10.1021/es070395n

Elimination of Mercury by Yellow Perch in the Wild

2007· article· en· W2086159358 on OpenAlexafffund
Jillian L. A. Van Walleghem, Paul J. Blanchfield, Holger Hintelmann

Bibliographic record

VenueEnvironmental Science & Technology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsTrent UniversityUniversity of ManitobaFisheries and Oceans Canada
FundersManitoba Hydro
KeywordsBioaccumulationMethylmercuryPerchMercury (programming language)Environmental chemistryFish <Actinopterygii>ChemistryEnvironmental scienceFisheryBiology

Abstract

fetched live from OpenAlex

The rate of methylmercury (MeHg) elimination by fish is important in determining the extent of bioaccumulation and for predicting recovery times of MeHg-contaminated fisheries. Rates of MeHg elimination remain uncertain in existing bioaccumulation models due to a lack of field studies. We addressed this problem by monitoring fish that had naturally accumulated isotopically enriched MeHg (spike MeHg) during a whole-ecosystem experiment. We transported yellow perch (Perca flavescens) from the experimental lake to an untreated lake and monitored spike total mercury (THg, most of which was MeHg) losses over 440 d. Spike THg was distributed among fish tissues in a similar way as ambient THg (background non-spike THg). We observed rapid loss of spike THg from liver and other visceral tissues (approximately 90 d) followed by a plateau. Subsequently, there was prolonged redistribution of spike THg into muscle (180 d). Loss of spike THg from the whole fish occurred > 5 times slower (half-life of 489 d) than in past laboratory studies using this species. We determined that MeHg bioaccumulation models with laboratory-based elimination rates produced faster losses than those observed in wild fish. The present findings provide support for refining elimination rates in MeHg models and show the importance of examining biological processes under natural conditions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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.

Study designBench or experimental
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

Citations81
Published2007
Admission routes2
Has abstractyes

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