Designing Gardens with Flora of the American East, Revised and Expanded
Bibliographic record
Abstract
As recent years have seen alarming declines of insect and bird populations in many states, more gardeners have discovered the importance of including native plants in order to nurture these pollinators and sustain local ecosystems. But when so many popular landscaping designs involve exotic cultivars and invasive plant species, how can you create a garden that is both aesthetically pleasing and ecologically responsible? In this fully revised second edition of the classic guide Designing Gardens with Flora of the American East , gardening expert Carolyn Summers draws on the most recent research on sustainable landscaping. She is joined in this edition by her daughter, landscape designer Kate Brittenham, offering an intergenerational dialogue about the importance of using indigenous plants that preserve insect and bird habitats. The practical information they provide is equally useful for home gardeners and professionals, including detailed descriptions of keystone trees, shrubs, perennials, vines, and grasses that are native to the eastern United States. Accompanied by entirely new illustrations and updated plant lists, they offer chic yet eco-friendly landscape designs fully customized for different settings, from suburban yards to corporate office parks. The states covered in this book are CT, DE, IA, IL, IN, KY, MA, MD, ME, MI, MN, MO, NC, NH, NJ, NY, OH, PA, RI, TN, VA, VT, WI, and WV, as well as southern Quebec and Ontario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".