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Record W4410722171 · doi:10.3390/toxics13060432

Metal Exposure, Bioaccumulation, and Toxicity Assessment in Sediments from the St. Lawrence River Before and After Remediation Using a Resuspension Technique

2025· article· en· W4410722171 on OpenAlexafffund
Masoumeh Javid, Catherine N. Mulligan, Marie‐Paule Lefranc, Maikel Rosabal

Bibliographic record

VenueToxics · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsBioaccumulationEnvironmental chemistryEnvironmental remediationEcotoxicologySedimentHyalella aztecaBioavailabilityEnvironmental scienceChironomus ripariusPollutantContaminationChemistryEcologyBiologyAmphipodaMidgeCrustacean

Abstract

fetched live from OpenAlex

, evaluated the effects of exposure to heavy metal-contaminated sediments collected from the study area under three conditions: before remediation, after remediation, and suspended particulate matter (SPM). The selected toxicity tests allowed for the evaluation of biological responses across varying concentrations of heavy metals. Statistical analysis revealed no significant differences in survival or growth between sediment-exposed organisms and controls for either species. In addition, bioaccumulation of Cr, Ni, Cu, Zn, As, Cd, and Pb in both organisms was assessed and compared among the sediment conditions and the control. No statistically significant differences in tissue metal concentrations were found between organisms exposed to sediments from the study area and those in control conditions. Sequential extraction analysis indicated that a substantial proportion of metals in the sediments were bound in stable, non-bioavailable forms. These findings are consistent with the observed biological responses, as low levels of bioavailable metals corresponded with the absence of toxic effects. Together, the data confirm that the sediments, regardless of remediation stage or particle fraction, posed no significant biological risk under the conditions tested.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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