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Record W2099842040 · doi:10.4319/lo.2001.46.5.1141

Spatio‐temporal geochemistry of mercury in waters of the Tapajós and Amazon rivers, Brazil

2001· article· en· W2099842040 on OpenAlexaff
M. Roulet, Marc Lucotte, René Canuel, N. Farella, Y. G. De Freitos Goch, José Reinaldo Pacheco Peleja, Jean Remy Davée Guimarães, Donna Mergler, M. Amorim

Bibliographic record

VenueLimnology and Oceanography · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMethylmercuryTributaryMercury (programming language)Environmental scienceAmazon rainforestAquatic ecosystemEnvironmental chemistryHydrology (agriculture)Drainage basinWater columnSoil waterEcosystemSurface waterEcologyOceanographyBioaccumulationGeologyChemistryGeographyEnvironmental engineeringSoil scienceBiology

Abstract

fetched live from OpenAlex

Spatial and temporal variations of mercury (Hg) concentrations were monitored in the surface waters from the lower portion of the Tapajós River, the Arapiuns River, its principal tributary, and the Amazon River at its confluence with the Tapajós. In the rivers, Hg concentrations in the water column are governed by the concentration of suspended particles. Hg in the filtered water showed very little seasonal variation with low concentrations in the lower Tapajós (<1.8 ng L −1 ), Arapiuns (<0.8 ng L −1 ), and Amazon (<2.8 ng L −1 ). Concentrations of fine particulate Hg (0.6–29.7 ng L −1 ) represent 40%–90% of the total volumetric concentration of Hg. In relation to their oxyhydroxide contents, suspended particles are not richer in Hg than nearby soils where oxyhydroxides control the accumulation of Hg. The study shows that the dominant stock of Hg in the aquatic ecosystems of this region is derived from erosion of natural soils in the catchment rather than from anthropogenic pollution. The input of natural Hg coming from soils into the aquatic ecosystems may have increased over historical levels in the region. This increase of total Hg in aquatic ecosystems could potentially account for high levels of methylmercury recently reported in fish and humans in the lower Tapajós River area, but the link between the different processes that promote high levels of methylmercury exposure for the human community remains to be proved.

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 score0.007
Threshold uncertainty score0.311

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 head0.007
GPT teacher head0.218
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

Citations65
Published2001
Admission routes1
Has abstractyes

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