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Record W2123506747 · doi:10.2174/1874940201003010038

Assessing PCR Inhibition from Humic Substances

2014· article· en· W2123506747 on OpenAlexaff
Carney Matheson, Carli Gurney, Neal Esau, Ryan Lehto

Bibliographic record

VenueThe Open Enzyme Inhibition Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsLakehead University
FundersCollege of Computing
KeywordsChemistryEnvironmental chemistryEnvironmental scienceComputational biologyBiology

Abstract

fetched live from OpenAlex

Inhibition remains the greatest methodological challenge in molecular analysis of buried biological remains. Inhibitory compounds associated with soil environments comprise primarily of humic acids and fulvic acids, collectively referred to as humic substances. We examined the sensitivity of 13 DNA polymerases to both humic acids (11ng-110 g) and fulvic acids (9.4ng-94 g) and the concentration at which successful amplification can be achieved. This research identified that all 13 DNA polymerases tested exhibited inhibition with varying concentrations of humic acids and that 5 out of the 13 DNA polymerase tested exhibited inhibition with varying concentrations of fulvic acid. The most tolerant DNA polymerase to inhibition due to the presence of humic and fulvic acids is pfu DNA polymerase followed by KlenTaq LA DNA polymerase and RealTaq DNA polymerase that were both only inhibited by 11 g and 110 g of humic acids. In addition, we present the use of size exclusion chromatography to remove small molecular weight humic substance, dramatically increasing the success of molecular analysis on material associated with burial. This research has implications to the fields of environmental microbiology, soil science, forensic science and archaeological science.

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 categoriesScience and technology studies, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.031
GPT teacher head0.258
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; 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

Citations144
Published2014
Admission routes1
Has abstractyes

Explore more

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