Notice bibliographique
Résumé
In times of budget constraints a municipal tree budget cannot be emotional but must be based on economic reality. Urban trees as part of the urban infrastructure must be part of the decision process. This paper suggests that municipal arborists use a two-pronged approach for the municipal budgeting process. Urban infrastructure is defined by the dictionary as the base facilities, equipment, services and installations for the growth and functioning of a Urban trees are as important as the roads, sidewalks and equipment that is needed for the growth and functioning of a city. are or should be considered part of the urban infrastructure and treated as such during the municipal budgeting process. Unfortunately, this is not the case in most cities. The tree care budget is among the first cut because trees can take care of themselves. Municipal arborists know that this is not true and must now make others aware of the benefits of urban tree care. Tree Value The value of urban trees can be either an asset or a liability to a People like trees; in a poll (2) people were asked to choose among 26 things that they considered important to their happiness. Ninety-five percent wanted green grass, trees, and flowers because of their aesthetic and psychological qualities (Figure 1). More important than humans trees is their need of plants. Most Americans live in urban areas and have a psychological need of urban greenery. Lederer (5) writes that Trees and other plants in the environment can be 'preventive medicine' to reduce stress, boredom, and some other problems of daily life. They are also being used successfully in the treatment process to overcome specific emotional conditions and help improve the life quality. As a result of people's need and liking of street trees, the trees add monetary value to property (Figure 2). This in turn increases the tax base. As the former municipal arborist of New York City, I never found a run-down neighborhood that was well stocked with trees. New York City conservatively valued its city trees at $2 billion in 1983 (3). Dr. Brian Payne of the U.S. Forest Service studied 800 properties and found that trees could increase the value of a property by as much as 20% (5). In turn, municipal arborists/urban foresters have to use this information to increase their working budget. Increased Street Tree Budget = More & Better = Increased Tax Base can also have a negative effect on a city's budget. Improper planting and maintenance can greatly increase the cost of municipal government. Proper selection of tree species and sites could reduce or even eliminate utility line pruning and sidewalk/curb damages (Figure 3) caused by tree roots. The utility line clearance cost to CON ED for Westchester, New York is $2.2 million per year to trim and remove trees along 16,300 miles of overhead wires (1). Figure 1. Properly planned, planted and maintained trees are an asset and beauty for a This information can be used to sell your budget. 1. Presented at the annual conference of the International Society of Arboriculture in Quebec City, Canada in August 1984. Journal of Arboriculture 11 (4): April 1 985 113 TREES COULD MAKE A DIFFERENCE IN THE SELLING PRICE OF YOUR HOME
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 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 ».