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Record W7041316523

Canadian Seal Hunt

2016· book-chapter· lt· W7041316523 on OpenAlexaboutno aff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2016
Typebook-chapter
Languagelt
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsSeal (emblem)Fur sealLegislationGovernment (linguistics)Animal rightsLimitingFish <Actinopterygii>
DOInot available

Abstract

fetched live from OpenAlex

Seal hunting (variously known as 'harvesting', 'slaughter', and 'killing'), or sealing, is currently practiced in eight countries, with most of the world's hunting taking place in Canada.The seal hunt is surrounded by controversy due to the clash between animal rights and environmentalist concerns and economic interests.In the 1960s, protesters pressured the Canadian government to pass legislation limiting the killing.Since then, killing quotas were introduced.The hunt has led to wide-spread protest by animalrights activists as well as other concerned groups, and by some international governmental institutions.Conservationists have demanded reduced rates of killing, arguing that the hunt is cruel as well as threatening to the very survival of the seals.There are two main reasons for the Canadian harp seal hunt: the seal products and the hunters' desire to keep the seals from eating the fish stocks on the eastern seaboard.Most sealing in Canada occurs in late March in the Gulf of St. Laurence and in the northeast of Newfoundland in April.Harp seals, the main species hunted, are called "hair seals", which depend on their blubber as their defence against the coldtheir pelts (skin with fur) have no underfur.The most seals killed are those under four months old that have just grown out of their white-coat stage.According to Sea Shepard Conservation Society Canada sells pelts to eleven countries, with Norway, Germany, Greenland, and China purchasing the largest quantities.Economic, cultural and environmental factors, as well as climate change, affect seal hunting.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.012

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 head0.011
GPT teacher head0.169
Teacher spread0.158 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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