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Evaluating the effects of historic bottleneck events: an assessment of microsatellite variability in the endangered, North Atlantic right whale

2002· article· en· W1706703097 on OpenAlexafffund
R. C. Waldick, Scott D. Kraus, Moira W. Brown, Bradley N. White

Bibliographic record

VenueMolecular Ecology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcMaster University
FundersNational Marine Fisheries ServiceNatural Sciences and Engineering Research Council of CanadaTrent UniversityWorld Wildlife Fund
KeywordsPopulation bottleneckBiologyRight whaleMicrosatellitePopulationGenetic diversityGenetic variabilityEndangered speciesGenetic variationZoologyEffective population sizeLoss of heterozygosityAlleleEcologyEvolutionary biologyWhaleGeneticsDemographyGenotype

Abstract

fetched live from OpenAlex

Commercial exploitation reduced the North Atlantic right whale (Eubalaena glacialis) population from c. 12,000 in the 11th century to around 300 by the 21st century. We examine the effect of this population decline on levels of genetic variation at 16 microsatellite loci and contrast levels of variability to that in a closely related species (E. australis). Of the 13 loci developed from the E. glacialis genome, 100% were polymorphic in E. australis. In contrast, nine loci were polymorphic in E. glacialis and four were fixed. Both allelic diversity (A) and heterozygosity (H) were significantly lower in E. glacialis than E. australis (A = 3.2 +/- 2.6 vs. A= 6.9 +/- 3.3, P < 0.001; H= 0.31 +/- 0.25 vs. H= 0.72 +/- 0.23, P < 0.001, respectively). Bottleneck anlayses indicate that the population is in mutation-drift equilibrium and that a genetic bottleneck did not occur during the most recent decline (18th-20th centuries). Nevertheless, low frequency alleles are relatively uncommon in E. glacialis, suggesting that genetic variability has been reduced in this population. Possible origins of low genetic variability are discussed, including the slow but continual erosion of alleles during the 800-year period of decline.

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 score0.014
Threshold uncertainty score0.836

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)0.0010.000

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 head0.291
Teacher spread0.274 · 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

Citations86
Published2002
Admission routes2
Has abstractyes

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