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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

CANADIAN SEAL HUNTSeal 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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0070.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0890.015

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; a candidate call from one source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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