10. The Golden Eagle - Conservation & Protection in Kingston, Ontario
Bibliographic record
Abstract
This research project works to analyze and diminish the major threats to the Golden Eagle species through developing efficient and effective conservation techniques and introducing these advancements into the Kingston, Ontario habitat. We propose a partnership with the Kingston Field Naturalists (KFN), a community group with an active mandate in the preservation and conservation of wildlife and natural habitats. Many of their current projects involve at-risk bird species, and they are well equipped to aid in the successful development and implementation of this initiative. There are a few factors that affect the livelihood of Golden Eagles. Wind turbines, pesticides and power lines are some parts of an urban setting that cause disturbance to these creatures. Other vulnerabilities include habitat destruction, limited food availability and human killings to prevent preying on livestock. Some conservation techniques that are successful in managing Golden Eagle populations around the world include the use of bird sensitivity maps and the implementation of adaptive-management frameworks during community planning. Sensitivity maps are formulated taking into account foraging range, collision risk and sensitivity to disturbance (Bright et al., 2008), while adaptive-management frameworks limit recreational activities near known nesting areas (Fackler et al., 2010). By implementing and adapting strategies put in place in countries like Ireland and around the world we hope to reintroduce a sustainable population of Golden Eagles in Ontario, specifically within the Kingston area. This can be achieved through donation of Golden Eagle chicks from areas in Canada in which this bird is common.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".