Estimating breeding bird survey trends and annual indices for Canada: how do the new hierarchical Bayesian estimates differ from previous estimates?
Bibliographic record
Abstract
Canadian data from the North American Breeding Bird Survey (BBS) provide information on the population status and trends for over 300 species that regularly breed in Canada. Since the first assessments were made in the mid-1970s, both the dataset and the suite of statistical tools and techniques available to researchers have grown. As a result, Canadian BBS trend estimates have been derived from numerous statistical models. Because the BBS data are relatively complex, different statistical models can generate different trend and status estimates from the same data. In 2013, Environment Canada’s Canadian Wildlife Service began producing BBS status and trend estimates using a hierarchical Bayesian model. To give users of BBS trends and annual indices of abundance a better understanding of these estimates, we demonstrate and explain some of the similarities and differences between the new hierarchical Bayesian estimates and those from the previous model; discuss the philosophical and inferential consequences of estimating trends with the new model; and describe how the hierarchical Bayesian model differs from the model currently used in the United States. Overall, trends and annual indices from the new model are generally similar to estimates from the previous model; however, they are more precise, less variable among years, better represent the spatial variation across Canada in population status, and allow for more intuitive and useful assessments of uncertainty.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".