Understanding Urban Tree Ecophysiology Worldwide: Unveiling the Urban Trees Ecophysiology Network (UTEN)
Bibliographic record
Abstract
With the ongoing surge of urbanization, a majority of the world's population now resides in urban areas exposed to various environmental stressors. Cities, experiencing temperatures up to 10°C higher than nearby rural areas due to energy consumption and urban infrastructure, necessitate urgent measures to address these challenges, especially in the context of climate change and projected urban population growth. In this landscape, the amplification of tree canopy cover emerges as a potent instrument, with the potential to elevate the quality of urban life significantly. Trees, with their multifaceted benefits—including the reduction of energy consumption, reducing thermal stress and local temperatures via shading and transpiration, mitigation of air pollution, and the overall enhancement of well-being—stand as indispensable contributors. However, the resilience of urban trees is constantly tested by a wide range of abiotic and biotic stressors, accentuated by the formidable impacts of climate change, jeopardizing their functionality, productivity, and survival, reducing their cooling potential and other ecosystem services. Therefore, understanding the intricate relationship between urban environments and the ecophysiology of trees is crucial for addressing climate change, promoting urban forest health, and making informed decisions. To tackle that, the Urban Tree Ecophysiology Network (UTEN) has been established as a global collaboration platform involving researchers, stakeholders, and municipalities. UTEN aims to investigate two fundamental questions: how the urban environment affects tree functionality and health, and how trees modify the microclimate of cities at different biomes. Employing a comprehensive campaign-based approach, accompanied by high-resolution IoT sensors, we continuously measure trees' transpiration, growth, and diameter changes, as well as the surrounding temperature and relative humidity. Additionally, seasonal physiological measurements are employed to assess tree health and functionality. These shared and aggregated data empower researchers to address common questions related to tree health and stress in the face of a changing climate. Furthermore, network nodes can leverage the accumulated knowledge to explore site-specific inquiries tailored to their own urban realities. Through cultivating international collaboration and robust data sharing, UTEN is committed to optimizing the ecosystem services rendered by urban trees. This expansive network, currently encompassing 12 cities across multiple continents, is dedicated to deepening our comprehension of the intricate interplay between trees and the urban environment, thereby paving the way for a more resilient and sustainable urban future. In our presentation, we will share the preliminary outcomes gleaned from approximately one year of meticulous measurements, offering initial insights and preliminary conclusions drawn from these initial findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".