Gardening with Native Plants of the Pacific Northwest
Bibliographic record
Abstract
The Pacific Northwest abounds with native plants that bring beauty to the home garden while offering food and shelter to birds, bees, butterflies, and other wildlife. Elegant trilliums thrive in woodland settings. Showy lewisias stand out in the rock garden. Hazel and huckleberry number among the delights of early spring, while serviceberry and creek dogwood provide a riot of fall color. Gardening with Native Plants of the Pacific Northwest is the essential resource for learning how to best use this stunning array. Close to 1,000 choices of trees, shrubs, perennials, annuals, and grasses for diverse terrain and conditions, from Canada to California, and east to the Rockies 948 color photographs, with useful habitat icons Fully updated nomenclature, with an index of subjects and an index of plant names (common and scientific) New to this edition: chapters on garden ecology and garden science Appendix of Pacific Northwest botanical gardens and native plant societies Glossary of botanical, horticultural, and gardening terms With enthusiasm, easy wit, and expert knowledge, renowned botanist Art Kruckeberg and horticulturist Linda Chalker-Scott show Northwest gardeners, from novice to expert, how to imagine and realize their perfect sustainable landscape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.026 |
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".