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Record W2843025488 · doi:10.1139/er-2018-0021

Polar bear research: has science helped management and conservation?

2018· article· en· W2843025488 on OpenAlexafffundvenue
Dag Vongraven, Andrew E. Derocher, Alyssa M. Bohart

Bibliographic record

VenueEnvironmental Reviews · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaCanadian Wildlife FederationArcticNetQuark ExpeditionsWorld Wildlife Fund
KeywordsWildlifeUrsus maritimusWildlife managementGeographyEnvironmental resource managementPeer reviewThematic mapEcologyPolitical scienceBiologyCartographyEnvironmental scienceArctic

Abstract

fetched live from OpenAlex

Wildlife management is predicated upon the use of scientific research to assist decision-making. However, assessment of the effectiveness of the management–research relationship is rarely undertaken. Polar bears (Ursus maritimus) have benefitted from an international agreement that required each of the countries within the species’ range to manage them using the best available scientific data. The objective of this paper is to conduct a systematic review of peer-reviewed literature on polar bears to describe research trends and to assess how effectively research has met management needs. We analyzed 1191 peer-reviewed scientific papers from 1886–2016 covering 24 research topics. Annual counts of papers within each research topic were assessed for temporal trends, spatial coverage, and the extent to which they have facilitated management and monitoring needs. The annual number of papers increased from <10 in the early 1960s to >50 in recent years with a mean of 2.2 papers per subpopulation per year with great variation between the 19 global subpopulations. We conclude that there is an imbalance in the geographic and thematic focus of peer-reviewed research in recent years, and that only four subpopulations appear to have had a research focus covering most parameters essential for conservation and sound management.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.010

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 head0.121
GPT teacher head0.331
Teacher spread0.210 · 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
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

Citations16
Published2018
Admission routes3
Has abstractyes

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