Drivers of Low Taxonomic Diversity in Canadian Prairie Urban Forests and the Resulting Risk of Localized Mortality to Invasive Pests and Pathogens
Bibliographic record
Abstract
In the Canadian prairies, historical preferences for elm and ash have resulted in localized susceptibility to Dutch elm disease (DED) and emerald ash borer (EAB). This thesis examines the drivers and potential future impacts of low diversity in the street tree populations of five Canadian prairie cities. The historical and present drivers of low diversity were investigated using archival and interview data, respectively. Conceptual frameworks were constructed that identify both biophysical and human drivers constraining or enhancing street tree diversity. Using tree inventories from the study cities, the potential impacts of DED and EAB on distributional justice were simulated. The results indicate that while DED may improve distributional equality, EAB may worsen inequalities. Both DED and EAB may dampen – but do not eradicate – distributional inequities. This work underscores the importance of diversity in urban forest resilience, advocating for practices that mitigate the vulnerabilities to invasive pests and pathogens.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".