Bibliographic record
Abstract
A superb visual overview of the major public parks designed by the foremost landscape architect in American history.Winner, John Brinkerhoff Jackson Book Prize, Foundation for Landscape Architecture, FY16Lavishly illustrated with over 470 images—129 of them in color—this book reveals Frederick Law Olmsted's design concepts for more than seventy public park projects through a rich collection of sketches, studies, lithographs, paintings, historical photographs, and comprehensive descriptions. Bringing together Olmsted's most significant parks, parkways, park systems, and scenic reservations, this gorgeous volume takes readers on a uniquely conceived tour of such notable landscapes as Central Park, Prospect Park, the Buffalo Park and Parkway System, Washington Park and Jackson Park in Chicago, Boston's "Emerald Necklace," and Mount Royal in Montreal, Quebec. No such guide to Olmsted's parks has ever been published.Frederick Law Olmsted (1822–1903) planned many parks and park systems across the United States, leaving an enduring legacy of designed public space that is enjoyed and defended today. His public parks, the design of which he was most proud, have had a lasting effect on urban America. This gorgeous book will appeal to landscape professionals, park administrators, historians, architects, city planners, and students—and it is a perfect gift for Olmsted aficionados throughout North America.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.012 |
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".