Urban House Sparrow ( <i>Passer domesticus</i> ) populations decline in North America
Bibliographic record
Abstract
House Sparrow (Passer domesticus) populations declined across much of their global range in the late 20th century. Most research examining this decline is conducted in the species' native European range, but Europe encompasses a small portion of the species' current distribution. House Sparrow population trends in the United States and Canada, and the potential mechanisms driving these trends, remain relatively unexplored. We use 21 years of data from Project FeederWatch, a large-scale citizen science project, to investigate House Sparrow population trends in North America. We found winter flocks in urbanized areas were larger than flocks in rural areas, with widespread spatial heterogeneity in local population trends. Despite greater abundance in developed areas, House Sparrow populations declined in developed areas from 1995 to 2016 while remaining stable in rural areas. House Sparrow population declines coincide with an increase in populations and expansion of the winter distributions of Accipiter hawks, which are known predators of House Sparrows. However, we do not find a direct connection between the presence of Accipiter hawks at count sites and House Sparrow population declines in winter. These results expand our knowledge of widespread House Sparrow declines to North America and provide context for continuing research on House Sparrow declines in the introduced range.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".