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Quantification and genetic profiling of DNA isolated from free‐floating feces of the North Atlantic right whale (<i>Eubalaena glacialis)</i>

2008· article· en· W1992460497 on OpenAlexaff
Roxanne M. Gillett, B. N. White, Rosalind M. Rolland

Bibliographic record

VenueMarine Mammal Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsTrent University
FundersSociety for Marine Mammalogy
KeywordsRight whaleMicrosatelliteBiologyFecesGenotypingWhaleZoologyDNA profilingCetaceaGenotypeGeneticsVeterinary medicineDNAAlleleEcologyGene

Abstract

fetched live from OpenAlex

Abstract Fecal analysis from the highly endangered North Atlantic right whale provides valuable information about health and reproductive parameters of individual animals. Genetically profiling the feces facilitates this connection when the sample originator is unknown. Although genetic analysis of feces collected in terrestrial systems has become well established, genetic studies of cetacean DNA are rare. Here, the use of free‐floating feces as a source of right whale DNA and the reliability of the genotypes produced are examined. On average, fecal extracts yielded 25 ng of DNA/mg dry weight, but less than 1% was right whale DNA. Although all samples were amplified using genus‐specific mitochondrial control region primers, the quantity of right whale DNA present was over estimated when compared to amplifications using nuclear primers. No correlation was found between the quantity of right whale DNA recovered and the duration the sample sat in the water. Composite microsatellite profiles from multiple amplifications of 28 fecal samples of known origin were consistent with profiles of the same individuals obtained from skin biopsies, however, the rate of allelic dropout varied depending on the amount of right whale DNA added. A screening and genotyping protocol for reliable genetic profiling based on fecal DNA quantification is presented.

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 score0.019
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.014
GPT teacher head0.207
Teacher spread0.193 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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