The temporal development of old-growth structural attributes in second-growth stands: a chronosequence study in the Coastal Western Hemlock zone in British Columbia
Bibliographic record
Abstract
One of the key issues facing forest resource planners is the conservation and recruitment of old-growth characteristics in managed forests. The paucity of long-term data sets in many regions has limited our ability to project the temporal patterns of structural development in second-growth forests. Age-based thresholds have been employed in some jurisdictions, but these lack flexibility and are arbitrary in nature. Here we conduct a chronosequence study consisting of second-growth and old-growth stands in the coastal forests of Vancouver Island, British Columbia, to identify structural attributes that are suitable for quantifying and monitoring the progressive development of old-growth characteristics. The following structural attributes were identified and evaluated in the chronosequence analysis: volume and density of large live stems, standard deviation of stem DBH, density of large-diameter snags, volume of woody debris, and understory vegetation cover. The rate at which old-growth structural characteristics developed in second-growth stands varied considerably, with the earliest reaching levels observed in old-growth stands within 112 years, while most requiring 200 to greater than 250 years. The use of quantifiable measures of old-growth structure will help forest managers plan for the continued protection and recruitment of old-growth structure within managed forest landscapes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".