2. Correlations between Urbanization and Reproductive Fitness in Agelauis phoeniceus (Red-Winged Blackbird)
Bibliographic record
Abstract
Urbanization has complex and diverse effects on the environment. How do animals respond to such dramatic changes to their habitat? In this study, I investigated the relationship between the degree of habitat urbanization and reproductive success in Red-Winged Blackbirds. I located and sampled two populations of Red-Winged Blackbirds: one rural population located at the Queen’s University Biological Station in Elgin, Ontario, and one urban population located at Belle Park in Kingston, Ontario. I mapped nesting territories from both populations and evaluated them for parameters such as water, marshland, and vegetation cover, as well as distance to roads, trails, and buildings. I also took physical measurements from adult birds, eggs, and nestlings of each territory to evaluate reproductive fitness. With this data, I will compare the degree of habitat urbanization with reproductive success. I hope to see meaningful relationships that might reveal how breeding Red-Winged Blackbirds respond to urbanized landscapes, and what key components of nesting territory might influence their reproductive success. With our society currently experiencing the largest wave of urban growth in history, it is crucial to understand the effects of this growth on other species and how we can most effectively preserve ecological value in urban landscapes.
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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 | 0.000 | 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) | 0.001 | 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".