Development of a prototype modeling system to estimate the GHG mitigation potential of forest and wildfire management
Bibliographic record
Abstract
Having recently experienced the three worst wildfire seasons in British Columbia's history in 2017, 2018 and 2021, and anticipating more severe impacts in the future, a key Carbon (C) research priority is to develop reliable models to explore options and identify a portfolio of regionally differentiated solutions for wildfire and forest management. We contribute to this effort by developing a prototype integrated C modeling framework which includes future wildfires that respond to forest stand characteristics and wildfire history. Model validation evaluated net GHG emissions relative to a ‘do-nothing' baseline for several management scenarios and included emissions from forest ecosystems, harvested wood products and substitution benefits from avoided fossil fuel burning and avoided emissions-intensive materials. Data improvements are needed to accurately quantify the baseline and scenario GHG emissions, and to identify trade-offs and uncertainties. • A Fire Tolerant scenario included post-fire restoration with planting of climatically suitable fire-resistant species and salvage harvest in place of clearcut harvest.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".