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Record W4392924133 · doi:10.1656/045.031.0106

PCR-Based Amplification of a Cox1 Mini-DNA Barcode Gene from Feces: A Non-Invasive Molecular Technique to Identify Environmental DNA Samples of Maritime Shrew (Sorex maritimensis)

2024· article· en· W4392924133 on OpenAlexaffabout
Golnar Jalilvand, Donald T. Stewart

Bibliographic record

VenueNortheastern Naturalist · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsAcadia UniversityYork University
Fundersnot available
KeywordsShrewFecesEnvironmental DNABiologyBarcodeDNA barcodingDNASorexGeneZoologyGeneticsEcologyBiodiversityComputer science

Abstract

fetched live from OpenAlex

Sorex maritimensis (Maritime Shrew) is endemic to Canada and found only in Nova Scotia and New Brunswick. The Maritime Shrew has been identified as one of the vertebrate species in Nova Scotia that is most susceptible to the effects of climate change and global warming, and it is listed by NatureServe as vulnerable (category G3). While generally regarded as a wetland specialist, relatively little is known about its specific habitat preferences. Non-invasive methods of sampling have proven valuable in identifying and monitoring such rare species. The objective of this study was to optimize a non-invasive method to document presence of Maritime Shrews using collected fecal DNA and to develop a PCR-based protocol to amplify a short, ∼120 base-pair section of the cox1 gene using shrew-specific primers. We used baited feeding tubes to collect shrew feces. We designed cox1 PCR primers to preferentially amplify this mini-DNA barcode for shrews in samples that may contain feces from rodents as well. We designed the primers to amplify a small amplicon to increase the likelihood of successful amplification from degraded DNA. This technique is likely to be effective for documenting the distribution and habitat preferences of this relatively rare shrew in Nova Scotia and New Brunswick.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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.

Study designBench or experimental
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
Published2024
Admission routes2
Has abstractyes

Explore more

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