Restoration of School Campuses - A piece to the urban climate challenge
Bibliographic record
Abstract
As Pacific Northwest communities look for solutions to address Climate Change Action plans, one piece of the puzzle lies throughout our urban areas – school campuses. School districts tend to be one of the largest land owners across urban and suburban communities. Campus layouts and land management practices have trended to minimalize natural settings, beyond turf lawn, parking lot shade trees and street trees. City codes have provided some direction to reduce pavement heat islands and green stormwater management, but the majority of school campuses are pavement or turf. Initiatives are underway to change how school campuses look and function, both for educational and play uses, but also to support broader goals. Local school districts bond programs have updated building designs to be more efficient and reduce environmental impact. Similar thinking has been applied to the campuses, many of which range up to 20 acres per site. Greening of Schoolyard best design and management practices have added a variety of natural features to campuses, including shade trees, native plants, several garden types (including pollinators) and more. These designs not only significantly increase the amount of green corridors, they also become living laboratories for students to see their daily lesson plans come to life. They can observe, learn, understand, and perhaps become the next generation of stewards. I will showcase recent results from bond programs in Vancouver and Evergreen Public Schools over the last 5 years and the impact they are making to address sustainability goals.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".