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Record W7079508930 · doi:10.26108/ka0g-b652

Seals in western Hudson Bay: Assessing proportions in natural and human harvests using genetic methods

2017· article· en· W7079508930 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2017
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsUrsus maritimusBayHabitatArcticAbundance (ecology)PredationRelative species abundanceSea ice

Abstract

fetched live from OpenAlex

Arctic seals are of great importance to polar bears (Ursus maritimus) and humans, however the ecosystem that supports them is changing as the climate warms. In spite of the importance of seals, little is known about their abundance or the relative abundance of each species. The proportion of seals harvested by hunters and seals killed by polar bears could be used to infer the naturally occurring relative abundance in Hudson Bay. This study compares 104 seal samples harvested by hunters between 2014-2016 and 12 seal samples killed by polar bears in 2014. All samples are from Hudson Bay near Churchill, Manitoba. Analysis of the samples determined which species: harbour seal (Phoca vitulina), ringed seal (Pusa ispida), and bearded seal (Erignathus barbatus) are killed by polar bears and humans. I developed a restriction enzyme digest to determine the species of these samples, quickly and cheaply. I found that there was no difference (p=0.998, Freeman-Halton extension of the Fisher's exact test) between the proportion of seals harvested by humans or by polar bears, which suggests that both polar bears and humans are harvesting seals in the proportions with which they are encountered in this area. The data also provided insight into polar bear diet, in that they prefer ringed seals over bearded and harbour seals. Understanding predators and their prey in the Arctic is important as climate warming and changes occur in sea ice habitat that seals and polar bears rely on.

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 score0.097
Threshold uncertainty score0.577

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 communication0.0010.001
Open science0.0010.000
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 head0.062
GPT teacher head0.391
Teacher spread0.328 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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