Greenhouse gas emission effect of suspending slash pile burning in Ontario's managed forests
Bibliographic record
Abstract
Ontario has made a commitment to reduce its greenhouse gas (GHG) emissions by 15, 37, and 80% below 1990 levels by 2020, 2030, and 2050, respectively. Ontario's forest managers can contribute to meeting these targets by implementing changes to forestry practices that either reduce emissions from operations or increase carbon sequestration in forest ecosystems and harvested wood products. We present an analysis of the effects on GHG emissions resulting from suspending the current management practice of slash pile burning (burning harvest residue in the forest without energy recovery). The analysis was performed for each of Ontario's forest management units (FMU) with assumed suspension of slash pile burning for four different periods: 2016–2025, 2016–2050, 2016–2075, and 2016–2100. Annual and cumulative avoided emissions from suspending slash pile burning that would have occurred with current practices were estimated from planned harvest volume and area adjusted to reflect harvesting levels from 1990 to 2009, data on slash pile burning from 2008 to 2013, and emission factors for combustion and decay of wood estimated from the literature. Suspending slash pile burning was estimated to reduce GHG emissions by year 2100 in all four no-burn scenarios, with cumulative GHG emission reductions estimated at -0.7, -4.5, -14.1, and -33.4 Mt CO2eq (million tonnes of CO2 equivalent), respectively. At the same time, suspending slash pile burning for the above-listed four periods resulted in losses of forest area by 2100 estimated at 7200, 24000, 40800, and 57800, respectively. The accuracy of these projections is affected by uncertainty in estimates of several components of the analysis, of which the primary one is the historical rate of slash pile burning. Improvement in measuring and reporting procedures is needed to obtain more reliable estimates of the amount of slash burned.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".