Bird of Prey Migration in the Greater Toronto Area
Bibliographic record
Abstract
Topography is known to factor into the migration patterns of birds of prey. As topography changes to reflect changing land use and urbanization, it becomes important to assess migrating species biodiversity. In this paper I attempt to evaluate how Lake Ontario, as a topographic barrier for the southbound autumn migrating birds of prey, impacts local bird of prey biodiversity. Using data collected by volunteers from four Hawk Watch groups in the Greater Toronto Area I evaluated species richness and diversity for each of the sites. In this, I found that Cranberry Marsh had the greatest Shannon-Weiner diversity index values among the four groups. It is therefore the site with the greatest biodiversity, a result contrary to my hypothesis. I followed this analysis with a comparison of species between three sites: High Park, Cranberry Marsh, and Iroquois Shoreline. Overall, I found a great amount of consistency between all sites, rather than High Park reporting the greatest numbers which was expected. Given the proximity of each study site to each other this result suggests a strong tendency for successful repeatability using the conventional Hawk Watch methodology. Further studies on methodological accuracy, as well as integration of citizen science generated knowledge for use in ecological studies are possible points of investigation to build upon for future research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".