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Record W7070535911

Predicting the future summer range of a declining Arctic waterfowl: the Greenland White-fronted Goose (Anser albifrons flavirostris)

2022· article· en· W7070535911 on OpenAlexaboutno aff

Bibliographic record

VenueHogskolan Ihalmstad (Halmstad University) · 2022
Typearticle
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGooseRange (aeronautics)ArcticClimate changeSnowThe arctic
DOInot available

Abstract

fetched live from OpenAlex

This study investigates what the future might hold for an endemic goose species, found exclusively in the low Arctic region of West Greenland. Facing a decline since four decades, the Greenland White-fronted Goose constitutes one of few goose populations showing a declining trend. Warmer temperatures on the breeding grounds and improved winter survival seem to benefit most populations of geese. Here, a species distribution model is applied to predict how the environmental conditions of low Arctic Greenland might change in the future. A ‘business as usual’ scenario, projected with two different climate models, indeed showed improved future conditions within the summer range of the Greenland White-fronted Goose. Snow cover, followed by temperature, was found to be the most important environmental factor shaping the distribution. Furthermore, novel potential breeding grounds were located on the southeast coast of Greenland, opposite the current summer range. This area is discussed in terms of becoming a future refuge for this vulnerable population, away from the newly established Greater Canada Goose in West Greenland. Interactions between the two species seem to have increasingly negative effects on the breeding success of Greenland White-fronted Goose. Whether the novel breeding grounds will eventually become colonized or not remains highly uncertain. However, other goose populations have shown such range shifts to be possible.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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