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Record W2782953094 · doi:10.1039/c7em00486a

Mercury methylation in stormwater retention ponds at different stages in the management lifecycle

2018· article· en· W2782953094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsThe Scarborough HospitalUniversity of TorontoToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMercury (programming language)StormwaterEnvironmental scienceStormwater managementEnvironmental chemistryMethylmercurySurface runoffEcologyChemistryBiologyComputer scienceBioaccumulation

Abstract

fetched live from OpenAlex

Stormwater retention ponds effectively manage erosion, flooding, and pollutant loadings, but are also sources of methylmercury (MeHg), a bioaccumulative neurotoxin which is produced by anaerobic aquatic microorganisms. Stormwater retention ponds have a 10-15 year working life, after which they are dredged and reflooded. In this study, we related MeHg biogeochemistry to the different stages of the management lifecycle. In a new, a dredged, and a mature stormwater retention pond, we measured MeHg and inorganic mercury (IHg) concentrations, and the potential for MeHg formation (Kmeth), during the early summer, peak summer, and fall of 2013. In our study sites, MeHg concentrations appear to be driven by mercury (Hg) methylation, indicated by significant correlations between Kmeth values and MeHg concentrations and the percent of Hg present as MeHg. Relationships between Hg variables and ancillary biogeochemistry suggest that Hg methylation is carried out by sulfate reducing bacteria, but that the process is modulated by the supply of IHg substrate, sediment total and labile organic carbon, and possibly competition with nitrate reducers. Wetlands at different points in the management lifecycle differ in terms of their MeHg biogeochemistry. The organic matter-poor new wetland had low MeHg production (mean Kmeth 0.014 per day) and sediment concentrations (mean 0.015 ng g-1), while the mature wetland both produced and accumulated MeHg about five times more actively. Methylmercury production capacity was only temporarily reduced in the reflooded sediments of the dredged wetland, which experienced rapid increases in Kmeth values from low (mean 0.015 per day) immediately after dredging, to values similar to those in the mature wetland after five months. This pattern may have been related to recolonization of the sediments with mercury methylators or increased microbial activities in response to the addition of fresh organic matter. Additional studies should focus on the applicability of these patterns to stormwater retention ponds in other areas, and particularly investigate the effects of stormwater pond dredging on their microbial ecology and MeHg biogeochemistry.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.016
GPT teacher head0.265
Teacher spread0.249 · 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