Using landscape metrics to measure suitability of a forested watershed: a case study for old growth
Bibliographic record
Abstract
Several metrics for spatial heterogeneity based on distribution of stands suitable for old growth were calculated for the actual and optimal conditions of a watershed in the Medicine Bow National Forest in Wyoming. Optimal conditions were based on expert opinions. The actual condition was compared with target conditions using a multivariate method called profiling, which develops profiles based on various spatial statistics and examines the similarity of these profiles using a multidimensional scaling (MDS) procedure. Profiles for various target landscapes clustered together in MDS space, and this space could be defined and quantified using a kernel density estimator. The distance from the centroid of the target space to the position of the actual stand is used as a measure of dissimilarity. By comparing the condition of a given watershed to that of what experts envisioned would be optimal, we argue that the relative condition of the watershed can be characterized. We make a distinction between stand-scale metrics and landscape-scale metrics. We propose that this method may be useful in quantifying changes in landscape conditions and could be useful as a monitoring method in forest plans.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".