Examining the effect of diverse management strategies on landscape scale patterns of forest structure in Pennsylvania using novel remote sensing techniques
Bibliographic record
Abstract
We used novel remote sensing techniques to compare the landscape-scale patterns of forest structure in Pennsylvania, USA under the management of four different agencies with varying primary objectives, including production forestry, wildlife habitat, recreation, and private ownership. We (i) developed a forest structure classification scheme using publicly available LiDAR and orthographic aerial imagery data, (ii) mapped the forest structure across twenty forested landscapes, and (iii) compared the landscape-scale forest structure patterns among the four forest management types. Our results indicate that different management philosophies and their associated forest management approaches have resulted in contrasting landscape-scale patterns of forest structure. Privately managed forests had shorter forests, higher densities of distinct patches, higher interspersion of patch types, and higher forest structure diversity at fine-scales (1.5 ha grain size) compared to forests lightly managed for recreation. Production forests under ecosystem management and forests managed for wildlife habitat exhibited intermediate patterns of forest structure. This variation in forest structure patterns among the forest managers is likely to have implications for wildlife habitat and other ecosystem services. Furthermore, greater emphasis is needed on encouraging private landowners to manage across property boundaries and providing the resources and tools to manage forests at the landscape scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".