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Record W2410126667 · doi:10.34080/os.v10.22886

Changes in numbers and distribution of staging and wintering goose populations in Sweden, 1977/78—1998/99

2000· article· en· W2410126667 on OpenAlexaboutno aff
Leif Nilsson

Bibliographic record

VenueOrnis Svecica · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
FundersNaturvårdsverket
KeywordsGooseBrantaPopulationAnatidaeGeographyWaterfowlBiologyEcologyZoologyDemographyHabitat

Abstract

fetched live from OpenAlex

Regular goose counts have been undertaken in Sweden since 1977/78 as a part of the International Goose Counts organised by Wetlands International. The main counts are undertaken in October and November, covering all sites of importance for Bean Goose Anser fabalis and in January when all sites are covered. September counts of Greylag Goose Anser anser will be published separately. In October, the major part of the World population of the Taiga Bean Goose Anser fabalis fabalis is found in Sweden. The population increased from about 20,000 in 1960 to 80,000 in 1989, after that it has decreased to about 50,000. During the study period Bean Goose numbers decreased markedly in the southernmost part of Sweden, whereas numbers increased at sites further north in southern Sweden. The species also established new important staging areas. This change in distribution was probably related to differences in hunting pressure between different regions and to changes in agriculture. Staging populations of White-fronted Goose Anser albifrons also increased during the period reflecting changes in the much bigger population south of the Baltic. Marked increase in numbers and a spread to new sites were also noted among staging Greylag Geese, Canada Geese Branta canadensis and Barnacle Geese Branta leucopsis reflecting increased breeding populations in south Sweden.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.742

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)0.0000.000

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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2000
Admission routes1
Has abstractyes

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