Effects of Seasonal Timber Harvesting Restrictions on Procurement Practices
Bibliographic record
Abstract
Abstract Wisconsin's forest products industry relies on a consistent supply of sustainably produced timber for its mills; however, recent research suggests significant seasonal variation in timber sale availability. We conducted a survey of Wisconsin mills to examine their procurement practices and assess how seasonal timber harvesting restrictions (STHRs) affect the forest products industry. Fifty-seven mills responded to the survey, which represented a 40 percent response rate. Respondents processed approximately 75 percent of the state's annual roundwood production. The average procurement radius ranged from 75 miles for small sawmills to over 120 miles for pulp mills. Peak inventory levels exceeded 30 days during each quarter for both pulp mills and sawmills, and peak inventory levels during the first quarter exceeded 60 days. Respondents reported that STHRs were common in the state and mills had adjusted their procurement practices in response. Pulp mills estimated that STHRs cost each firm an average of nearly $2.7 million annually, or $4.93 ton−1 of wood purchased during the year, whereas small sawmills reported average additional costs of $188,888 per firm ($10.33 ton−1). Seasonal weight limits on public roads, oak wilt restrictions, and access and transportation restrictions on individual timber sales were reported to have the greatest impact on mills. Continued cooperation is needed among foresters, landowners, and the forest industry to apply STHRs in a manner that protects the forest resource while maintaining a consistent and sustainable supply of timber to the forest industry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".