The greening of urban post-industrial landscapes: past practices and emerging trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public, private, and non-profit entities are increasingly engaged in greening post-industrial landscapes in an effort to achieve a broad array of aesthetic, infrastructure, recreational, ecological, and economic development objectives at various scales. Despite this growing level of interest, however, these projects continue to face numerous challenges related to financing, land acquisition, soil contamination, and concern regarding long-term maintenance, just to name a few. This paper begins with an overview of the “nature” of greening activity that has taken place in the USA and Canada and then focuses on three case studies – Elmhurst Park New York City, South Waterfront Portland, and Menomonee Valley Milwaukee – in order to illustrate the planning processes involved in their remediation and development. Key lessons are then drawn, with a particular emphasis on the growing need to attract buy-in and funding by linking greening with other forms of development and broader urban sustainability initiatives.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it