Vancouver’s Urban Forests : Gauging Public Perceptions and Using Citizen Science to Monitor Ecological Health
Bibliographic record
Abstract
Healthy urban forests improve human health, provide a variety of ecosystem services, and support plant and animal diversity. It is critical to monitor the health of urban forests due to the anthropogenic stressors they face and their importance in urban environments, and citizen science has shown to be a valuable tool to accomplish this goal (Galloway, Tudor, & Vander Haegen, 2006). Citizen science involves engaging ordinary citizens to volunteer their time to collect scientific data, often in the form of educational events or meetups. The city of Vancouver, Canada, has a large urban forest, with canopy cover making up 18% of the city area; however, canopy cover is declining due to development (City of Vancouver, 2014). The Vancouver Park Board is interested in increasing awareness about urban forests through educational opportunities, such as citizen science programs, that encourage the public to take part in helping to monitor the health of their local forests. To meet these goals, this project engaged citizens in Vancouver to reveal perceptions of urban forests, developed and tested a citizen science method to monitor aspects of urban forest health, and provided recommendations on how citizen science programs could be developed in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".