The dynamics of landscape change and snow geese in mid‐continent North America
Bibliographic record
Abstract
Abstract The Mid‐Continent Population of the lesser snow goose, which breeds in the eastern and central Canadian Arctic and sub‐Arctic, and winters in the southern United States and northern Mexico has increased 5–7% annually from the late 1960s to the mid‐1990s, largely because of increased survival in response to an agricultural food subsidy. The rise in numbers complements the increased use of nitrogen fertilizers and a corresponding rise in yields of rice, corn, and wheat along the flyways and on the wintering grounds. In sub‐Arctic migration areas and at Arctic breeding colonies, foraging by high numbers of birds has led to loss of coastal vegetation, adverse changes in soil properties and the establishment of an alternative stable state of exposed sediment, which can be detected with LANDSAT imagery. At a local scale, gosling growth, size and survival decreased in affected areas and other taxa have been adversely affected. The food subsidy on wintering and migration areas appears insufficient to meet reproductive demands as foraging in spring continues to occur on southern Hudson Bay staging and nesting areas. The recent introduction of liberal hunting regulations may reduce population size in the near term, but the revegetation of these coastal ecosystems will take decades to achieve. The present pattern of vegetation loss in these Arctic coastal systems is likely to continue in the forseeable future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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.
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".