Characterization and Justification of Trees on an Inner-City Golf Course in Halifax, Canada: An Investigation into the Ecological Integrity of Institutional Greenspace
Bibliographic record
Abstract
Institutional greenspaces such as golf courses, cemeteries, military bases, hospitals, and university campuses are not generally revered for their ecological integrity. The existence of golf courses in particular has been heavily debated due to widespread perceptions of these spaces as environmentally degrading. Though much of the total area of golf courses is occupied by heavily manicured lawns, Canadian golf courses tend to be well treed and thus show significant potential to enhance forest coverage and contribute to the conservation of native tree species when established on previously unforested land. To explore this potential, a tree inventory was carried out on an inner-city golf course in Halifax, Nova Scotia, and findings compared to an earlier inventory of more naturalized (i.e., ingrowth) forest areas in the same city. Based in the Acadian Forest Region, this case study used the characteristics of a healthy and mature Acadian Forest as a model for ecological integrity. It was found that both the golf course and the ingrowth populations were largely representative of a mixedwood Acadian forest. Likewise, both populations were in a similar stage of regeneration and exhibited similar stresses. These results suggest that if improved forest management approaches are employed, golf courses will effectively strengthen the ecological integrity of urban forests. This is an especially important finding in the climate change era when tree populations are likely to be subjected to new environmental stressors which may be alleviated via the human intervention that is available on managed lands such as institutional greenspaces.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".