Assessing the influence of forest ownership type and location on roundwood utilization at the stump and top in a region with small-diameter markets
Bibliographic record
Abstract
Research conducted in a variety of hardwood regions across the United States has indicated that utilization of small-diameter roundwood is hindered by a lack of markets. Efficient removal of such material could enable silvicultural practices to improve stand conditions and economic return for landowners. However, evidence from other studies has suggested that markets alone may not be enough to encourage small-diameter utilization, and that management decisions are important as well. This study sought to compare roundwood utilization at the stump and top for different ownership categories and locations in north-central Wisconsin, a region with active pulpwood and other low-grade markets and different types of forest ownerships. Thirty-six recently harvested sites were visited in 2007 and 2008 across three ownership types (managed county-owned forests, private land timber sales involving a professional forester, and private land timber sales without involvement of a professional forester) and two locations (county groupings) with different markets. Results of a linear mixed model indicated that ownership type was a significant predictor variable for the utilization measures studied (stump diameter, top diameter, and stump height). Location effects were significant for stump diameter and stump height. Sales on unmanaged private forests exhibited the largest stump and top diameters and highest stump heights, regardless of location. Overall, this study suggests that both management practices and markets influence harvest site small-diameter utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".