Multiple-Use Zoning Model for Private Forest Owners in Agricultural Landscapes: A Case Study
Bibliographic record
Abstract
Many small-scale private forest owners increasingly focus their management on amenity functions rather than on wood production functions. This paradigm shift is an opportunity to implement novel forestry management approaches, such as forested land zoning. Forest zoning consists in separating the land base in three zones that have different management objectives: (1) conservation zones; (2) ecosystem management zones; and (3) intensive production zones, which locally increase productivity, as a trade off to increase the land area dedicated to conservation and ecosystem management. We evaluate the ecological feasibility of implementing forest zoning on a private property (216 ha) at St-Benoît-du-Lac, Québec (Canada) characterised by agricultural and forest land uses. As a basis for delineating conservation and ecosystem management zones, historical and contemporary data and facts on forest composition and dynamics were reviewed, followed by a detailed forest vegetation analysis of forest communities. Delineating intensive production zones was straightforward, as fertile agricultural field margins located downslope were used to establish multifunctional hybrid poplar buffers. At St-Benoît-du-Lac, a realistic zoning scenario would consist of (1) conservation zones covering 25% of the forestland (37 ha); (2) ecosystem management zones covering 75% of the forestland (113 ha, including restoration zones on 24 ha); and (3) intensive production zones on 2.8 ha. Based on a yield projection of 13 t/ha/year for hybrid poplars, only 5.6% of agricultural field areas would need to be converted into agroforestry systems to allow for the loss of wood production in a conservation zone of 37 ha of forest. Ecosystem services provision following the implementation of zoning would include increased habitat quality, biodiversity protection and enhancement (by restoration of some tree species), carbon storage, non-point source aquatic pollution control, local biomass production for heating, and increased forest economic value.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".