MétaCan
Menu
Back to cohort
Record W4399176926 · doi:10.1016/j.envpol.2024.124232

Key factors influencing Hg levels and trends in unperturbed oligotrophic temperate and boreal lakes

2024· article· en· W4399176926 on OpenAlexafffund
Martin Pilote, Daniel Houle, Christian Gagnon, Suzanne Couture, Ashu Dastoor, Andrei Ryjkov

Bibliographic record

VenueEnvironmental Pollution · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsEnvironmental scienceMethylmercurySnowmeltDissolved organic carbonBiotaEnvironmental chemistryBorealDrainage basinTerrestrial ecosystemMercury (programming language)Temperate climateSnowEcosystemSurface waterDeposition (geology)Hydrology (agriculture)EcologyStructural basinChemistryGeologyGeographyBioaccumulation

Abstract

fetched live from OpenAlex

Mercury (Hg) is a toxic metal that presents a major risk to ecosystems, biota, human health, and remains a priority concern. In temperate and boreal lakes Hg and methylmercury (MMHg) are expected to vary as a function of atmospheric Hg deposition, lake water chemistry, catchment characteristics and climate variables. The aim of this study was to quantify Hg and MMHg in unperturbed oligotrophic lakes and to identify the factors controlling their distribution. We first hypothesized that lake Hg (and MMHg to lesser extent) spatial variations are linked to atmospheric deposition, catchment characteristics, and terrestrial exportation of dissolved organic carbon (DOC). We secondly examined if lake Hg concentrations have followed the decrease in atmospheric Hg emission observed between the mid-1990s to the end-2010s. We found that overall, atmospheric Hg has little impact on lake Hg and MMHg concentrations, which are both primarily influenced by DOC input originating from the forest catchment. The relationship between DOC and Hg differed between the spring and the fall, with a Hg-to-DOC ratio twice as high in spring. This seems related to snowmelt input of Hg (with a relatively reduced input of DOC) or the internal lake build-up of Hg during the ice-covered period. Of the 10 lakes intensively visited over a 20-year period, only 3 showed significant lake Hg decreases despite significant negative trends in atmospheric Hg concentrations, suggesting a lag between atmospheric and surface water temporal trends. Overall, terrestrial catchments retain around 80% of atmospheric Hg implying that large Hg pools have been built up in soils in the last decades. As such, the reduction of atmospheric Hg alone will not necessarily result in Hg decreases in lakes, since the Hg concentrations may be modulated by DOC export trends and catchment characteristics. This stresses the need to improve our understanding of the processes governing Hg transfers from catchments into 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 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental PollutionSame topicMercury impact and mitigation studiesFrench-language works237,207