Breeding Biology of Atlantic Population Canada Geese in Nunavik, Northern Québec
Bibliographic record
Abstract
The Atlantic population of Canada Geese (Branta canadensis interior) experienced a sharp decline in numbers in the late 1980s. Management agencies in Canada and the United States responded by implementing several measures, notably closing sport hunting seasons for a number of years in most Atlantic Flyway states and provinces and funding a research project to study the nesting ecology and the factors affecting productivity of this goose population. In this paper we present the nesting phenology and breeding biology of Atlantic population Canada Geese on their tundra nesting grounds in Nunavik, Québec, specifically on a primary study area (32.8 km2) along Hudson Bay (1997–2003) and on several secondary sites (most smaller than 1 km2) distributed along the coastal lowlands of Hudson Bay and Ungava Bay (1996–2005). In the late 1990s the population rebounded, with strong increases in the population of breeding pairs and the density of nests between 1996 and 2001, followed by stabilization of both variables from 2001 to 2005. As a result, there was a near doubling in productivity index (the number of goslings produced per km2) on the primary study area, from 17.9 in 1997 to 32.0 in 2003. Geese start laying eggs soon after snow disappears from their nesting grounds. On the primary study area, for all years pooled, mean clutch initiation date, clutch size, hatching date, and Mayfield nesting success were 27 May, 4.54 eggs, 26 June, and 67.3%, respectively. Among secondary sites along both Hudson Bay and Ungava Bay, we found a correlation between the annual average daily temperature for 4–24 May (the period leading up to egg-laying) and the annual mean clutch initiation date: higher temperatures were correlated with earlier initiation. Similarly, an earlier annual mean clutch initiation date was correlated with large mean clutch size.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".