Bibliographic record
Abstract
The North American Forests: Geography, Ecology, and Silviculture describes where, why, and how the many kinds of trees found on this continent grow in silvical associations - called forest cover types. Thirteen chapters describe more than 100 forest cover types, involving several times that many species. Diverse woodlands discussed include: o The Arctic tundra o Florida's tropics o The Atlantic's coastal pond pines o The Pacific's Monterey pines o The summits of Englemann spruce o Sea-Level swamps of baldcypress The text acts as a singular guidebook for specialists and students in natural resource disciplines examining the geography, ecology, and silvicultural practices for sustaining North American forests; students in curriculum's involving regional silviculture; and persons examining the goods and services from this varied, fascinating renewable resource. Benefiting from the author's five decades of practicing forestry, the reader will trek into virtually every "neck of the woods" - perusing exceptional field notes and photographs of the continent's forests. Featureso Offers a summary of forests in North America, ecological positions, and best management approaches for the benefit of mankind o Contains a readable language for both college students and professionals o Provides information covering the forests of Canada and the US o Lists "Further Readings" and "Subjects for Discussion and Essay" at the end of each chapter o Includes more than 100 photographs Audience o Foresters o Ecologists o Natural Resource Managers o Forestry Students
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.023 |
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".