The effects of domestic cat (Felis catus) density on urban bird abundance and richness
Bibliographic record
Abstract
Cat (Felis catus) predation is considered the greatest causes of bird mortality worldwide.While there is no doubt cats prey on birds, their effects on bird populations is uncertain.I predict the effect of cats should be greatest on birds that are less than 150 grams on average, nest or feed on or low to the ground, feed at bird feeders, or are migrants.I tested these predictions using Ottawa Bird Count (OBC) bird surveys and cat density estimates at 58 sites within residential Ottawa.I compared bird abundance and species richness with cat density for all birds and those hypothesized to have a strong or weak effect of cats for each trait, while controlling for amount of bird habitat (vegetation).Surprisingly I found cat density had very little effect on bird abundance or species richness, irrespective of species trait.Migrants were the only group that showed a significant effect of cats.In contrast to inferences from previous mortality estimates, my results suggest cats have little impact on urban bird abundance and richness, at least in urban regions where cat density is relatively low and cats spend a large part of the year indoors.vegetation.Regression coefficients, standard errors and p-values are presented for each model.Significant effects are denoted by (*)...................
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".