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Record W1599033642 · doi:10.1111/conl.12009

Rapid ecosystem change and polar bear conservation

2013· article· en· W1599033642 on OpenAlexaff
Andrew E. Derocher, Jon Aars, Steven C. Amstrup, Amy Cutting, Péter K. Molnár, Martyn E. Obbard, Ian Stirling, Gregory W. Thiemann, Dag Vongraven, Øystein Wiig, Geoffrey S. York

Bibliographic record

VenueConservation Letters · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsWorld Wildlife Fund CanadaYork UniversityMinistry of Natural Resources and ForestryTrent UniversityUniversity of Alberta
Fundersnot available
KeywordsClimate changeHabitatPopulationEnvironmental resource managementEcosystemArcticEcologyMarine ecosystemGeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Abstract Anthropogenic global warming is occurring more rapidly in the Arctic than elsewhere, and has already caused significant negative effects on sea ice‐dependent species such as polar bears. Although observed effects have thus far been gradual, the large amount of annual variation in the climate system may cause habitat changes in individual years that exceed the long‐term trend. Such years may be below critical thresholds necessary for feeding and result in unprecedented reductions in survival, reproduction, and abundance in some populations. Here, in anticipation of sudden negative population‐level effects, we provide an overview of proactive conservation and management options. Preplanning, consultation, and coordination of management responses will be necessary to reduce the risks to human safety and other effects of catastrophic declines in habitat. Advance consideration of the costs, legality, logistical difficulties, likelihood of success, and invasiveness of potential responses will be critical to minimizing short‐term negative effects while laying the groundwork for longer‐term conservation objectives.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.037
GPT teacher head0.208
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations105
Published2013
Admission routes1
Has abstractyes

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