Preservation of old‐growth forests: a case study of Big Timber Park, Whistler, BC
Bibliographic record
Abstract
This paper explores the concepts of old‐growth forests, preservation and natural disturbance and demonstrates how contemporary biogeographic theory contributes to successful preservation of old‐growth forests. The case study analyzes the composition, structure, age and growth histories of trees in Big Timber Park, Whistler, British Columbia. Composition. The structure of the forest was diverse and tree ages ranged from 122 to 305 years. The densities of large old Douglas‐fir, shade‐tolerant trees and all snags are indicative of old‐growth forests; however, maximum tree diameters and densities of large snags and logs do not meet quantitative criteria for old‐growth forests. The abundance of tree regeneration and understory vegetation are relatively low. We conclude that some aspects of Big Timber Park are transitional between the mature and old‐growth stages of forest development. In the future, fine‐scale canopy gaps caused by tree senescence and interactions among insects, pathogens and wind are expected to dominate stand dynamics. Forest structure will become increasingly complex, exemplifying the dynamic nature of old‐growth forests. To successfully preserve Big Timber Park and La préservation des forêts other old‐growth forests in Canada requires understanding of natural disturbances. Preservation and the criteria and indicators used to measure successful preservation must explicitly include elements of natural change.
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 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".