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Record W2020236718 · doi:10.7557/3.2855

Management of Atlantic walrus (<i>Odobenus rosmarus rosmarus</i>) in the arctic Atlantic

2014· article· en· W2020236718 on OpenAlexaffabout
Øystein Wiig, Erik W. Born, Robert E. Stewart

Bibliographic record

VenueNAMMCO Scientific Publications · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSubsistence agricultureArcticGeographyFisheryPopulationStock (firearms)The arcticRange (aeronautics)Human settlementEcologyOceanographyArchaeologyBiologyDemographyAgriculture

Abstract

fetched live from OpenAlex

We review the management of Atlantic walruses (Odobenus rosmarus rosmarus) past and present in the four range states—Canada, Greenland, Norway and Russia—which have permanent populations of Atlantic walruses. Populations in all four countries have been depleted, although the extent of depletion is not well known. Inuit in Arctic Canada and Greenland hunt Atlantic walruses for subsistence while they have been protected at Svalbard (Norway) since 1952 and in the western Russian Arctic since 1956. Since the second half of the 20th Century Canada and Greenland have increased protection of their walrus. Generally the number of walruses landed in Canada is governed by the number of hunters and/or people in the settlement and not by stock-specific quotas. Although quotas have been set in few communities, it is not known if they are adequate to prevent overhunting. A quota system for walrus hunting in Greenland began in 2006. The current control system is largely effective in ensuring the quotas are applied and that reporting is correct. Greenland currently sets quotas based on recommendations from scientific assessments using recent population estimates to allow population growth from a depleted population. A challenge with respect to managing walrus hunting remains the variable and sometimes high rates of lost animals. Since the 1960s changes in socio-economics in hunting areas of Arctic Canada and Greenland (and the use of snowmobiles instead of dog sleds in Canada) have led to a general decrease in interest in hunting of walruses and reduced harvest on walrus stocks in these countries. Although there is an active ongoing cooperation between Canada and Greenland scientists regarding assessments of shared populations of walruses currently there is no formal agreement between the two range states on co-management of shared stocks. Protection of walrus from other anthropogenic impacts generally focusses on large-scale industrial activity. The level of protection afforded to walrus habitat in many areas depends entirely on the rigor with which the Environmental Impact Assessments are conducted. Basic information on walrus such as numbers and stock discreteness is often lacking and sufficient lead-time is required to collect baseline data. Moreover, although most environmental protection legislation considers ‘cumulative impacts’, practical application remains problematic. The effectiveness of environmental protection regulations depends on industry compliance and the management authorities’ ability to enforce compliance. Because walrus are found in remote locations, enforcement remains a challenge. Increased human activity allowed by the current change in distribution and quality of arctic sea ice poses new threats to walrus if not well regulated. International agreements have varying importance for management within and among member states. Regulations governing international trade serve to identify illegally obtained products and to encourage range states to have a sustainable quota system. International cooperation in information sharing has had clear benefits for management of walruses in the past. The maintenance and expansion of these international efforts will improve the management of Atlantic walruses in the future.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.235
Teacher spread0.218 · 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

Citations27
Published2014
Admission routes2
Has abstractyes

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