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Record W2983331495 · doi:10.1111/mms.12652

Using pre‐ and postexploitation samples to assess the impact of commercial whaling on the genetic characteristics of eastern North Pacific gray and humpback whales and to compare methods used to infer historic demography

2019· article· en· W2983331495 on OpenAlexafffund
Stephanie L. Béland, J Darling, Timothy R. Frasier

Bibliographic record

VenueMarine Mammal Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsAsia Pacific Foundation of CanadaSaint Mary's University
FundersClayoquot Biosphere Trust
KeywordsWhalingHumpback whaleCoalescent theoryGenetic diversityPopulation bottleneckBottleneckBiologyFisheryGeographyEcologyWhalePopulationDemographyPhylogenetic tree

Abstract

fetched live from OpenAlex

Abstract Many species of whales went through recent bottlenecks due to commercial whaling. These declines were rapid and recent relative to the life spans and generation times of these species, raising questions regarding to what degree commercial whaling influenced the genetic characteristics of these populations. We analyzed mitochondrial and nuclear DNA from pre‐ and postwhaling samples from two populations that have arguably shown the greatest degree of recovery: eastern North Pacific gray and humpback whales. We also compare the performance of different methods to test for historic bottlenecks and infer past demography based on genetic data. We found substantially higher levels of genetic diversity in gray than in humpback whales (for both time periods), likely due to recent connectivity between Atlantic and Pacific gray whale populations. Other than mitochondrial diversity in humpback whales, levels of diversity were not lower in contemporary samples relative to prewhaling samples, indicating that commercial whaling had a minimal impact on metrics of genetic diversity themselves. However, it did have large impacts on the patterns of diversity, as evidenced by all coalescent‐based methods showing clear evidence of a bottleneck for both populations, whereas all but one method not based on the coalescent failed to detect a bottleneck.

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 score0.046
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.101
GPT teacher head0.346
Teacher spread0.245 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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