Bibliographic record
Abstract
Abandoned rail lines are always a challenge and many of them can add great value to our old American Cities. As we move into a new age development, urbanites are seeing the benefit of public spaces. Former rail lines make for great parks because the existing right-of-way has great connection. These connections allow for a healthier community, getting people to school and work faster while providing great chances to engage in recreation. The newly opened High Line in New York City is a perfect example of converting a rail line into a healthy public space. The High Line was an abandoned elevated railroad line and with community push was converted into a magnificent linear park. It has become such a destination for the community and tourist alike. Chicago is looking into a similar idea of creating a park along the Bloomingdale Trail. The elevated linear park would be 3-mile-long and connect neighborhoods, the river and Chicago. After Katrina communities saw the benefit of the Lafitte Corridor as a Linear Park for New Orleans that would connect many very different neighborhoods. The Lafitte Corridor runs from the Mid-City neighborhood all the way down to the French Quarter. In each of these three case studies, cities are taking serious look at the potential effects of redeveloping abandoned rail lines into great public spaces that allow for recreational and economic redevelopment.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.172 | 0.028 |
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".