Bibliographic record
Abstract
"For every element that we design in the landscape, there is a corresponding grading concept, and how these concepts are drawn together is what creates a site grading plan. This study guide explores these concepts in detail to help you learn how to grade with confidence in preparation for the Grading, Drainage and Construction Documentation section of the Landscape Architecture Registration Examination (LARE). This updated second edition is designed as a textbook for the landscape architecture student, a study guide for the professional studying for the LARE, and a refresher for licensed landscape architects. New to this edition: Additional illustrations and explanations for grading plane surfaces and warped planes, swales, berms, retention ponds, and drain inlets; Additional illustrations and explanations for grading paths, ramp landings, ramp/stair combinations and retaining walls; A section on landscape and built element combinations, highlighting grading techniques for parking lots, culverts and sloping berms; A section on landscape grading standards, recognizing soil cut and fill, determining pipe cover, finding FFE, and horizontal and vertical curves; Updated information about the computer-based LARE test; All sections updated to comply with current ADA guidelines; An appendix highlighting metric standards and guidelines for accessibility design in Canada and the UK. With 223 original illustrations to aid the reader in understanding the grading concepts, including 32 end-of-chapter exercises and solutions to practice the concepts introduced in each chapter, and 10 grading vignettes that combine different concepts into more robust exercises, mimicking the difficulty level of questions on the LARE, this book is your comprehensive guide to landscape grading"--
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.219 | 0.179 |
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".