Implications of alternative herbicide-use policies for forest management in Ontario
Bibliographic record
Abstract
Public sentiment is against herbicide use on public forests in Ontario. Provincial \npolicies are directing research into alternative vegetation management with only \nlimited interaction or support with forest resource based industries. The \ninitiative of this analysis was to substantiate or dismiss the hypothesis that a \nforest industry could feasibly regenerate a sound wood supply from a forest in \nNorthwestern Ontario under various herbicide-use limitations. Forest-level \nsimulation was used to produce 100-year forecast data for thirteen management \nscenarios, which covered current levels, reductions in area treated, restrictions \non how and where it could be applied, no use of herbicides, and a shift to a \nflexible wood supply. \nResults of the wood-supply analysis revealed that the company's wood-fibre \nneeds from the study forest could be maintained for all scenarios. Due to the \nage class structure of the forest and the reasonable harvest levels imposed by \nthe company, the most important component of the forest model was its \npresent volume. Thus, even under assumptions of decreased coniferous \nvolume production resulting from non-herbicide silvicultural treatments, only \nslight increases in harvest area were necessary 70+ years into the forecasts. \nThe wood supply, area treated with herbicides and silviculture cost response \nvariables provided the information required for sound decisions to be made for a \nlarge array of potential herbicide policy changes. Any strategy derived would \nneed to meet the new policy's requirements while minimizing impacts on wood \nsupply and silviculture costs and maintaining a desirable level of flexibility. For \nthe Seine River forest, a step-wise reduction in herbicide use was determined to \nbe the most appropriate strategy. This timing conforms well with forecasts of \nlow need for herbicide treatments and provides adequate time for research and development of environmentally sound, socially acceptable and economically \nfeasible alternatives to herbicides. \nThis strategy meets the 20% herbicide use reduction imposed in 1991 and sets \nthe company in a position to meet further changes. Impacts on both wood \nsupply and silvicultural costs were shown to be minor.
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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".