Understanding Urban Tree Ecophysiology Worldwide: Unveiling the Urban Trees Ecophysiology Network (UTEN)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».