Quantitative tracking of the vegetative integrity and distinctness of forested ecological communities: a case study of plantation impacts
Bibliographic record
Abstract
Ecological land classification (ELC) is central to forestry and environmental management. Few methods exist for the statistical confirmation of the distinctness and continued integrity of the ecological character of ELC regions. Consequently, forest managers lack the tools to measure the impact of ecosystem stressors such as harvest practices and climate change. We develop a framework for tracking the distinctness and modification of vegetative communities of ELC natural regions. We base the framework on principles of numerical taxonomy using the Kolmogorov–Smirnov measure of distributional difference. We demonstrate the utility of the framework using data from a 1986 Forest Development Survey of tree species abundances on 13 508 sample plots from natural regions of the New Brunswick, Canada, ELC. Using the framework, we found vegetative communities of the ELC statistically distinct in the 1986 sampling. We also investigated the impacts of plantations on forest composition using vegetation profiles from a 1999 Forest Development Survey of planted stands. Simulated planting to 20% levels suggests that past and projected planting practices will modify the vegetative character of several natural regions on a scale comparable with interregion variation. The results demonstrate the potential of the framework to track changes to a variety of biotic communities impacted by natural and anthropogenic disturbance.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".