Bibliographic record
Abstract
Forest industries in the eastern U.S. rely heavily on family forest owners to supply fiber needs for their mills. As of 2017, 79 percent of West Virginia is classified as forestland and of this roughly 86.5 percent is privately owned. With such a heavy reliance on wood from private forest lands, family forest owner satisfaction is extremely important if companies want to continue harvesting or working with these landowners in the future. Timber transactions are complex. No two timber transactions are exactly the same. They often involve many different parties apart from the landowner. Site conditions, land cover, and landowner goals all are major factors that influence the outcome of a timber harvest. A timber harvest can leave a property completely transformed. For better or worse the property will not be the same as before the harvest. Timber harvests are common in West Virginia many lack the use of a forester. Without a forester, landowners are likely at a competitive disadvantage when negotiating timber contracts and accomplishing their goals and future of their properties. The goal of this study was to explore ways to alleviate common pitfalls that lead to legal or financial issues that are associated with timber transactions. We carried out a mail-based survey to landowners who had recently harvested timber from their West Virginia properties. In this paper, we explore the relationships between landowner satisfaction with a harvest, their property attributes, management goals, and the types of professional assistance they received during their timber transaction. Many attributes selected to represent conditions and events during timber transactions were found to be significantly related to the overall satisfaction of landowners following timbering operations. The adequacy of several of the attributes were used as indicators of landowners’ perception of service quality. Having a forestry professional assist with the timber sale enhanced the likelihood that landowners would be satisfied with timbering outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".