Range of natural variability: Applying the concept to forest management in central British Columbia
Bibliographic record
Abstract
The range of natural variability (RNV) is a concept relevant to maintaining biodiversity and resilience in managed forests. It is, however, a challenging concept both to describe and apply. Here, we refine the definition of RNV. We also discuss information and data sources required and the appropriate use of spatial and temporal scales. A new term, the apparent range of variability (ANV), is suggested to convey the dependency of estimates of the RNV on the temporal and spatial extent of available data sources. We offer a process for developing an RNV definition, applying it operationally, and integrating desired future conditions with social and economic values. We illustrate the challenges in defining and implementing the RNV concept with an example of the interior Douglas-fir (Pseudotsuga menziesii var. glauca) forests in Lignum Ltd.'s Innovative Forest Practices Agreement area in central British Columbia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".