Education Tools for Mitigating Human-Wildlife Conflict in Urban Areas
Bibliographic record
Abstract
This major project report represents a substantive effort to create educational tools and opportunities for a wide range of audiences in hopes that an increase in ecological knowledge will help reduce instances of human-wildlife conflict in urban areas. By increasing ecological knowledge, the hope is to inspire a greater sense of awareness and care for the wild animals with which we co-occupy space. Toronto Wildlife Centre is a registered charity and a designated wildlife rehabilitation and rescue centre, offering care to sick, injured and orphaned wildlife found by members of the public. Working collaboratively with Toronto Wildlife Centre has allowed me to gain a nuanced understanding of human-wildlife conflicts in urban areas and contribute meaningfully to their educational programs. This Major Project comprises a suite of tools with which to provide education surrounding human-wildlife conflict in urban areas to a wide range of people. The Bounce Back Fact Sheets are for members of the public who find themselves in the midst of human-wildlife conflict. The educational blog posts are for people who are interested in learning more about urban wildlife. Finally, the high school presentations are aimed at promoting a wider appreciation of wildlife, an understanding of human-wildlife conflict in urban areas and steps we can take in our communities to move towards a human-wildlife coexistence. Each presentation will include how they meet curriculum requirements, allowing for teachers to integrate wildlife education and community partnership, whilst operating within the parameters of the curriculum. These educational tools address the need for pre-emptive education as well as offer education to those who have already found themselves in a conflicting situation with wildlife. The topics covered in the blog posts offer ecological knowledge about local Ontario species, what can be expected from them, and how we can help them succeed in urban environments. Topics covered are seasonally appropriate and are aimed at providing a resource for people who wish to learn more about wildlife or as a reference when facing situations such as a turtle crossing a roadway or discovering a bird nest. This Major Project represents a multi-faceted approach to scientific outreach. The objective of this work was to raise awareness of urban wildlife and the difficulties they face to promote sustainable coexistence of humans and non-human animals in an increasingly urbanized world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.005 |
| Open science | 0.001 | 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 teacher head, 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".