Extracting long‐term patterns of population changes from sporadic counts of migrant birds
Bibliographic record
Abstract
Abstract Declines of many North American birds are of conservation concern. For almost 40 years, experienced birders have kept daily counts of migrant landbirds during visits to Seal and Brier Islands, both of which are off Nova Scotia's southern tip. Here we assess the utility of Generalized Additive Models (GAMs) to extract patterns of population change of a common migrant to Seal Island, the Ruby‐crowned Kinglet, while controlling for other influences including season, weather and effort. We also demonstrate, using counts of the Kinglet from Brier Island as well as counts of another common migrant, the Yellow‐rumped Warbler, how our GAM methods can combine data from different geographic areas or distinct species. Most existing analyses of similar long‐term data sets have used linear models to estimate trends. Our results and comparisons suggest that GAMs are a powerful way of extracting more information from such data. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 teacher head, 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".