Lesser Snow Geese, <em>Chen caerulescens caerulescens</em>, and Ross's Geese, <em>Chen rossii</em>, of Jenny Lind Island, Nunavut
Bibliographic record
Abstract
We surveyed the Lesser Snow (Chen caerulescens caerulescens) and Ross’s geese (Chen rossii) of Jenny Lind Island, Nunavut, using aerial photography in June 1988, 1998, and 2006, and a visual helicopter transect survey in July 1990. The estimated number of nesting geese was 39 154 ± SE 2238 in 1988, 19 253 ± 2323 in 1998, and 21 572 ± 1898 in 2006. In 1988 an estimated 2.7% of the nesting geese were Ross’s. The July 1990 population of adult-plumaged birds was 25 020 ± 3114. The estimated percentage blue morph among Snow and Ross’s geese was 19.0% in 1988, 25.1% in 1989, 23.0% in 1990 and 21.1% in 2006. Estimated pre-fledged Snow Goose productivity was 47% young in 1989 and 46% in 1990. Combined numbers of Snow and Ross’s geese on Jenny Lind Island grew over 250 fold, from 210 adults in 1962-1966 to 54 100 adults in 1985. Numbers subsequently declined, to 42 200 in 1988, 25 000 in 1990, 20 300 in 1998, and 26 400 in 2006. Population decline between 1985 and 1990 was consistent with anecdotal reports by others that die-offs of Snow Geese occurred in 1984, 1985 and 1989, and with our August 1989 fieldwork which found evidence of habitat degradation and malnourishment of young geese. In spite of limited food resources on Jenny Lind Island, the colony continued to exist in 2006 at near its 1990 and 1998 levels. Further studies there could provide insights for management of the overabundant mid-continent Snow Goose population and its arctic habitats.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".