Regional Climate Effect on Population Abundance of Major Duck Species in Interior Plains of Canada
Bibliographic record
Abstract
Population dynamics of ducks is a complex process influenced by both biotic and abiotic factors, and good understanding about the mechanisms behind this process is needed not only for the management of duck harvest but also for the conservation of wetland, which is under threat in the face of land change and climate warming. Basic time series analysis techniques, such as correlogram, partial correlogram and periodogram, cluster analysis and partial least squares regression (PLSR) are applied to the long-term breeding population data gathered by Waterfowl Breeding Population and Habitat Survey and monthly weather data from Environment Canada in order to explore the pattern in the duck abundance data and the effect of local weather on population dynamics of ducks.\n\nResults of above analysis show that the duck abundance of previous years has short but strong correlation with the duck abundance of following years suggesting a large part of variability in duck abundance is explained by the abundance of previous years, especially that of last year. There is also clear periodic pattern in the duck abundance data, but the length of the cycle varies both for duck species and ecozones suggesting separating duck species and ecozones for analysis is necessary. The effects of weather on duck populations appear weak or absent in most PLSR models; this is consistent with previous studies. More detailed information, such as age structure of the duck populations and interspecific interactions, will be needed for future modeling. Among the small number of PLSR models exhibiting the weather effect, results suggest potential mechanisms, such as the impact of weather on recharge and discharge of wetland basins and nest failure caused by flooding.
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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 | 0.001 | 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) | 0.002 | 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".