Effects of diameter-limit by-laws on forestry practices, economics, and regional wood supply for private woodlands in southwestern Ontario
Bibliographic record
Abstract
The agriculturally dominated Counties of Huron and Perth in southwestern Ontario regulate forest harvesting on private land through diameter-limit-based tree conservation by-laws. The rates of harvesting, along with the volume and value of timber sales and the type and quantity of tree marking were examined for the years 1997 to 1999. Although these harvests may form an important part of periodic farm income, at only 13% forest cover, these landscapes maybe further degraded by unsustainable forest harvesting practices. Based on the three study years, the mean annual area of forest harvested was found to be 4.4% of the total private forest landbase. The mean volume harvested from upland and lowland deciduous forest was 4666 and 6148 fbm/ha, respectively. Over-harvesting under a diameter-limit or hybrid method occurred in 8% of woodlot area with removal rates in excess of 10 000 fbm/ha. The most severe over-harvesting disproportionately targeted lowland woodlots, possibly compromising the ecological health of these often sensitive areas. Sugar maple, red/silver maple and ash were most commonly harvested at 33%, 31% and 21% of total species volume, respectively. On average, for standing timber, landowners received $680/Mfbm in the upland hardwood forests and $281/Mfbm in the lowland hardwood forests. On an area basis, mean price paid was $3680/ha and $1956/ha respectively on upland and lowland forests. Only 8% of the private land was harvested using single-tree selection or stand improvement (92% harvest was diameter-limit or a hybrid of same). Using a simple model, we found that woodlot owners comprising at least 74% of private woodland area would need to participate in forest harvesting in order to maintain the 1997 to 1999 partial harvest area rate of 2349 ha/yr. This rate may not be sustainable, given poor forest conditions in some areas, past management practices and a reduction in landowners interested in forest harvesting. Improvements are needed to bring the level of good forestry practice up by 62% to meet the rates that were being performed under pre-1994, free, provincial government private land forestry programs. Key words: private land forestry, forest harvesting, forest conservation by-laws, sustainable forest management, diameterlimit harvest, private woodlots
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".