BUILDXNG GREENER NEIGHBORHOODS Cheryl Kellin
Bibliographic record
Abstract
be % when taIking about where she chose to purchase her new home. ‘The trees arc what brought us here. You go into so many new developments and they’ve just plowed everything over.” At its crnrent rate of household growth, our country will need 13 million new homes by the year 2000. A small but vocal minority of developers and buildm have recognized that development can be made more compatible with the environment, and that this approach is good for business. A recent NAHB survey of builders found that pcople.are willing to pay more for homes with Wes. Almost half of the home buyers paid $3,000 more; and a quarter spent over SS,OOO more. Land development pcImanently changes the land and the environment. This issue extends beyond the urban areas as development pushes out into rural lands. The challenge is how to cx”e trees during devclopmcnt to retain benefits of natural systems. BENEFlTS OF OUR URBAN ECOSYSTEMS Studies show that trees in our urban ecosystems provide niunerous environmental benefits. They heIp reduce:;tomwater flow and air pollution, reduce summer energy bills through direct shading of bddings, provide wildlife habitaq and prevent sediment erosion into streams, However, t1.x value of trees and other natural resources are often overIooked in community. developmen;: because professionals lack information necessary to incorporate these resources into their dt:signs. FORESTS has developed a way to map, measure, and calculate the benefits of urban-CAN vegetation, which helps developers directly incorporate thc value of natd resources into their developmeut process. This technique, Caled an Urban Ecological Analysis (UEA), uses lowlevel aeriaI photography along with CTTygreen, desktop computer software program, to help developers understand their urban forest resources and take advantage of the benefits they provide. AMERICC~N FORESTS deycloped CITYgreen as an application of Archview, a GIS (Geographic Infbrmation Systcms) program. Using CITYpn, a developer can run an analysis for energy consemtion, stormwater, carbon, air pollutan~~, and stormwater duction. CITYgreen was developed from research produced by
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.030 |
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".