Forest Regeneration: Perceptions of Natural Resource Professionals in West Virginia
Bibliographic record
Abstract
It has generally been assumed that natural hardwood regeneration in West Virginia after a timber harvest or other disturbance will be abundant and successful. However, changes that are being observed in the seedling and sapling components of forest stands suggest that problems may exist with regeneration of desirable species. Factors affecting regeneration have been the topic of conversation among foresters and other natural resource professionals for years. To address the need for more information about this issue, we conducted a mail survey of natural resource professionals (NRPs) in West Virginia. The objectives of the survey were to determine how they perceive the quality of regeneration, their level of satisfaction with regeneration, the types of concerns they have, and the locations and spatial variability of their regeneration concerns. Almost half (49%) of 261 respondents reported they were dissatisfied with the regeneration they had observed. Eighty-nine percent had at least one concern, while 40% had three concerns. For two-thirds (66%) of NRPs, the trees they would like to see regenerate did not correspond to the trees they observed to actually regenerate most abundantly. In general, satisfaction with regeneration was highest in the southwestern, southern, and southeastern parts of the state.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".