Modelling Landscape-level Effects of Reduced Herbicide Use in Two Forests in Northern Ontario
Bibliographic record
Abstract
In Ontario, forest management planning requires that legal obligations to sustain forest composition and pattern, wildlife habitat, and other values are met, while at the same time addressing, for example, wood supply needs, costs, forest access, and stakeholder concerns. One of the latter is pressure to reduce herbicide use. Stand-level effects of vegetation management alternatives have been documented, but how these effects scale up to the landscape-level and affect the achievement of social, ecological, and economic objectives embedded in forest management plans (FMPs) remains uncertain. We applied nine modelling scenarios in the context of approved FMPs to explore the potential landscape-level effects of replacing herbicide use with an alternative (brush saw) for two large forests in northeastern Ontario. Results of non-spatial and spatial modelling over 60 years suggested that although herbicides are applied to only 25% to 34% of the harvested area in these forests, reductions in use would affect: (i) overall wood supply, with 14% to 44% less conifer and 6% to 17% less hardwood available; (ii) habitat supply, with less habitat for species preferring recent disturbances and more habitat for species preferring mature and older forest; (iii) costs, with wood transportation cost increasing by 16% to 20% and increased spending on silviculture; (iv) size and distribution of cutblocks and disturbance patches, with more small patches; and (v) the extent of the active road network, which would increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".