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Record W3171116655 · doi:10.34080/os.v20.22619

Trends in goose numbers wintering in Britain & Ireland, 1995 to 2008

2010· article· en· W3171116655 on OpenAlexaboutno aff
Carl Mitchell, Kendrew Colhoun, Anthony David Fox, Larry Griffin, Colette Hall, Richard Hearn, Chas A. Holt, Alyn Walsh

Bibliographic record

VenueOrnis Svecica · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersScottish Natural HeritageNorthern Ireland Environment Agency
KeywordsGooseGeographyIcelandicEcologyZoologyFisheryPopulationBiologyDemography

Abstract

fetched live from OpenAlex

Twelve migratory and native goose populations winter in Britain and Ireland and up to date information on their abundance and distribution is provided. Seven populations are increasing: Barnacle Goose (Svalbard, current estimate 26,900 birds), Barnacle Goose (Greenland 70,500), Pink-footed Goose (288,800), North West Scotland Greylag Goose (34,500), re-established Greylag Goose (50,000), Light-bellied Brent Goose (East Canadian High Arctic 34,000) and Light-bellied Brent Goose (Svalbard 3,270). Two populations appear stable: Taiga Bean Goose (432 at two sites) and Icelandic Greylag Goose (98,300). Three populations are decreasing: European White-fronted Goose (2,760) due to short stopping in mainland Europe, Dark-bellied Brent Goose (82,970), due to a recent population decline (due to poor breeding success) and short stopping, and Greenland White-fronted Goose (24,055) due to recent poor breeding success and, up to 2006, hunting. An estimated 120,000 migratory geese wintered in Britain and Ireland in 1960 compared to 500,000 in 2008. Despite many goose species demonstrating high degrees of site faithfulness (responding to safe roosts and regular food supply), shifts in winter distribution of several goose populations have occurred (notably Icelandic Greylag Goose).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.004

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 head0.010
GPT teacher head0.260
Teacher spread0.249 · 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 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

Citations14
Published2010
Admission routes1
Has abstractyes

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