Effectiveness monitoring for biodiversity: comparing 15 year old structural retention harvest areas to fires in boreal aspen
Bibliographic record
Abstract
Convergence of species composition in regenerating harvested areas and naturally disturbed forest is a critical component of forest management modeled after natural disturbances. We assessed convergence of birds, plants, and habitat structures in aspen (Populus tremuloides) stands harvested with structural retention by Alberta-Pacific Forest Industries Inc. (Al-Pac) 15 years ago with similar aged fire area, and examined a chronosequence of younger and older burned aspen stands from Alberta Biodiversity Monitoring Institute (ABMI) sites. Most habitat structures and many bird and plant species in the 15 year harvest areas were at levels similar to 20–40 year or >40 year fire areas. Snags, moss, and lichen cover, and a few groups of species were at lower levels in the harvest areas than comparable aged fire areas or older stands. Agglomerative clustering showed the plant community to be most similar to >40 year burned stands, with the bird community intermediate between >20 year and <20 year fire areas. A novel likelihood-based analysis of species estimated the 15 year harvest areas to have a community similar to forest sites with 36.8% human footprint, indicating substantial recovery of the harvest areas. Harvesting aspen stands with structural retention appears to be effective at allowing most biodiversity components to recover rapidly.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".