Phenology and knowledge mobilization about the importance of trees in urban environments in southern Quebec, Canada
Bibliographic record
Abstract
A phenology project was launched in 2020 in the Eastern Townships region of southern Quebec, Canada, with the help from students from Bishop’s University. Initially, one of the main goals was to boost local phenology observations and gather data that will become useful in the future to document the impacts of climate change. We also wanted to pair the pollen monitoring programme that exists in Sherbrooke since 2006 with directs phenological observations of the local vegetation. The other goal of TreeTraque is to increase people's awareness of the impacts of climate change on vegetation. Riding on the momentum generated by the recent adoption of the politique de l’arbre (tree- or greening policy) by the city of Sherbrooke and by the push from numerous conservation organizations within the region, we are now adding a knowledge mobilization component to the project. We wish to reach the genera public more broadly to educate them about the importance of trees in the urban environment: they combat the urban heat island effect, capture atmospheric pollution, and enhance the esthetics of a neighbourhood. A few greening programs are already in place in Sherbrooke, however, they only aim at planting trees on institutional, industrial and commercial lots. It appears important to raise awareness among the general population, especially homeowners. Indeed, in some neighbourhoods, we see that numerous owners choose not to have trees on their property or prefer shrubs or plants that do not provide any shade on buildings, streets or driveways. Our long-term objective is to create more shade to help combat the urban heat island effects and reduce the impacts of future heat waves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".