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Climate change and the ecology and evolution of Arctic vertebrates

2012· review· en· W1678759898 on OpenAlexaff
Olivier Gilg, Kit M. Kovacs, Jon Aars, Jérôme Fort, Gilles Gauthier, David Grémillet, Rolf A. Ims, Hans Meltofte, Jérôme Moreau, Eric Post, Niels Martin Schmidt, Glenn Yannic, Loı̈c Bollache

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsCenter for Northern Studies
FundersNorsk PolarinstituttConseil régional de Bourgogne-Franche-ComtéNorges ForskningsrådInstitut Polaire Français Paul Emile Victor
KeywordsArctic ecologyArcticClimate changeEcologyArctic vegetationEcosystemThreatened speciesTrophic levelGlobal warmingEnvironmental scienceHabitatBiologyTundra

Abstract

fetched live from OpenAlex

Climate change is taking place more rapidly and severely in the Arctic than anywhere on the globe, exposing Arctic vertebrates to a host of impacts. Changes in the cryosphere dominate the physical changes that already affect these animals, but increasing air temperatures, changes in precipitation, and ocean acidification will also affect Arctic ecosystems in the future. Adaptation via natural selection is problematic in such a rapidly changing environment. Adjustment via phenotypic plasticity is therefore likely to dominate Arctic vertebrate responses in the short term, and many such adjustments have already been documented. Changes in phenology and range will occur for most species but will only partly mitigate climate change impacts, which are particularly difficult to forecast due to the many interactions within and between trophic levels. Even though Arctic species richness is increasing via immigration from the South, many Arctic vertebrates are expected to become increasingly threatened during this century.

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)0.001
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: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.195
GPT teacher head0.352
Teacher spread0.157 · 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

Citations234
Published2012
Admission routes1
Has abstractyes

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Same venueAnnals of the New York Academy of SciencesSame topicSpecies Distribution and Climate ChangeFrench-language works237,207