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Record W143046059

VEGETATION'S IMPACT ON URBAN INFRASTRUCTURE

2006· article· en· W143046059 on OpenAlexaboutno aff
Dennis P. Ryan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsLiabilityBusinessHappinessUrban forestryUrban forestGreen infrastructureAsset (computer security)Value (mathematics)Environmental planningProcess (computing)GeographyForestryFinancePolitical scienceComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.005
GPT teacher head0.233
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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