Embracing the future for and with urban forests and trees.International Urban Tree Diversity Conference UTD5 Book of Abstracts
Bibliographic record
Abstract
As our cities face the pressing challenges of climate change, extreme weather events, increasing population growth, and urban densification, the role of urban forestry becomes increasingly vital. The 5th Urban Tree Diversity Conference (UTD5) aimed to help craft a vision for the future of urban forests by sharing innovations from a spectrum of scientific disciplines that seek to secure the health and resilience of our urban trees. UTD5, building on the legacy from conferences in Alnarp/Malmo (Sweden), Melbourne (Australia), Vancouver (Canada), and St Petersburg (Florida, USA), offered an invaluable opportunity for urban forestry and arboriculture practitioners, researchers, policymakers, and stakeholders to come together and explore the future challenges and opportunities for our urban trees. By embracing emerging technologies, harnessing open data, and fostering citizen engagement, all in support of enhancing tree diversity, we can pave the way for greener and more resilient cities. UTD5 was held in October 2024 in Madrid, Spain, where we welcomed 150 participants from 26 countries around the world to help craft a vision for the future of urban forests by sharing innovations from a spectrum of scientific disciplines that seek to secure the health and resilience of our urban trees. The program included 8 keynote speakers, 44 oral communications, 1 round table, 16 poster presentations, and 3 technical visits. We truly think that UTD5 provided a valuable platform for sharing knowledge, exchanging ideas, and shaping the future of urban forests and trees to benefit our cities and our communities. We are confident that this vital work will continue at UTD6, and we look forward to meeting you all there again!
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".