A framework for urban–woodland naturalization in Canada
Bibliographic record
Abstract
Urban forest naturalization has gained momentum within municipal planning and non-governmental organizations. As the interest in naturalization increases, so does the impetus for exploring its conceptual and practical dimensions. Naturalization is a form of ecological restoration with prominent social dimensions. One motivation is reintegrating the urban forest to a greater extent into its surroundings, increasing the habitat available for native species. However, naturalization is not always desirable, as we may want to use and modify certain areas for cultural purposes, or feasible, as some species may be unable to establish in an urban setting. This paper examines the concepts underlying naturalization and how they influence naturalization decisions and goals. It then provides a framework for urban forest naturalness and explores potential applications of naturalness assessments in urban forest management. The framework outlines 37 different biotic and abiotic dimensions of naturalness that can help urban forest decision-makers visualize and manage the urban forest through understanding its individual parts and thus the whole. If a site is weak in particular dimensions, actions can be directed to increase the naturalness of these components. Similarly, some dimensions may be of more interest than others, such as increasing habitat suitability for a particular species. We then offer real and hypothetical examples of applying the framework to urban forest management. The benefits of a naturalized urban forest are many. It provides habitat for native species and has an important role as a biological teaching tool for urban residents. In developing this framework, we hope to expand the discussion on naturalization beyond simply planting native species and ceasing mowing to positioning urban forests in a broader landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".