Urban foresters’ perceptions about the role of soil and fungi in urban forest management and climate mitigation
Bibliographic record
Abstract
The benefits of soil and mycorrhizal fungi in mitigating climate impacts through their vital role in carbon sequestration are well recognised in the academic literature but are not well incorporated in current urban forest planning and policies. We conducted a survey to examine urban foresters’ perceptions about the incorporation of current scientific understanding of the role of soil and fungi, and stakeholders’ engagement in urban forest management. Overall, urban foresters perceive that soil and fungi are important, however, little consideration is given to their incorporation in urban forest management practices and policies. Many stakeholders’ engagement is low in urban forest management. Urban foresters perceive that decision makers have little knowledge about the role of soil and fungi which may pose barriers to its integration in planning and policies. The key challenges revealed by the survey include the lack of funding, conflicting priorities, and the lack of mechanisms to transfer scientific knowledge to urban foresters and inter-departmentally. Municipalities should develop policies that enhance knowledge transfer and integration to enhance the efficacy of urban forest’s strategies that work to support municipal climate goals. Furthermore, collaboration with diverse stakeholder groups may enhance communication and subsequently increase support for urban forest initiatives.
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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 | 0.003 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".