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Record W4399861094 · doi:10.1002/edn3.568

Metal ions limit or enhance environmental <scp>DNA</scp> detectability in marine sediments

2024· article· en· W4399861094 on OpenAlexafffund
L. Dumoulin, Marion Chevrinais, Richard St‐Louis, Geneviève J. Parent

Bibliographic record

VenueEnvironmental DNA · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversité du Québec à RimouskiFisheries and Oceans Canada
FundersFonds de recherche du Québec – Nature et technologiesFisheries and Oceans CanadaUniversité du Québec à Rimouski
KeywordsMetal ions in aqueous solutionLimit (mathematics)MetalIonEnvironmental DNADNADetection limitEnvironmental chemistryEnvironmental scienceChemistryOceanographyNanotechnologyMaterials scienceGeologyEcologyBiologyChromatographyMetallurgyBiochemistryBiodiversityMathematics

Abstract

fetched live from OpenAlex

Abstract Natural matrices affect environmental DNA (eDNA) detections. Effects of matrix‐eluted compounds on the polymerase chain reaction (PCR) step have been the focus of most inhibition studies. Factors affecting eDNA detections in a typical laboratory workflow, i.e., DNA extraction and PCR steps, are mostly unknown. Here, we assessed the effect of four metal ions (Ca 2+ , Fe 3+ , Mn 2+ , Cu 2+ ) present in marine sediments on DNA detectability for both the extraction and the PCR detection steps. A single metal ion and exogenous DNA were added to marine sediments treated chemically to remove inhibitors. Our results showed that natural concentrations of calcium, iron, and manganese ions in surface marine sediments can impede completely DNA detections. Alternatively, copper ions added to the matrix increased DNA detectability by 7.7%. We also observed bimodal inhibitory effects of calcium and iron ions on DNA detectability, suggesting that the extraction and the PCR steps are both affected. Our findings highlight new limitations of eDNA detections. Avenues to optimize eDNA detection protocols applicable to multiple matrices are discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.015

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 head0.010
GPT teacher head0.211
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2024
Admission routes2
Has abstractyes

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