Changes in numbers and distribution of staging and wintering goose populations in Sweden, 1977/78—1998/99
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".