Bibliographic record
Abstract
British Columbia, Canada just experienced two of the worst wildfire seasons in recent history - with climate conditions conducive to more severe fires forecast. This has led to a growing awareness by government of the need for a radically new approach to land management to protect resources and people. The goal of the Northern Wildfire Resilience Initiative (NWRI), led by the Bulkley Valley Research Centre (BVRC), is to facilitate forest and fire management paradigm shifts required to develop forest and community wildfire resilience. Key elements include partnerships, practices, policies and planning. The NWRI is an umbrella for many initiatives and projects. In April 2019, with strong support and funding from government and industry, the BVRC brought together over 160 people from local, regional and provincial governments, First Nations, local communities, NGOs, and tenure holders including large forest industry to develop a collaborative approach to reducing the risks of wildfires. A regional pilot project was developed - based on an analysis of the historic fire regimes, predicted climate change and current conditions. The outcome is an integrated approach that places priority on wildfire management activities, including timber harvesting and reforestation, that enhance wildfire resiliency. Fire management actions focus on actions that help return the forests to a state similar that found prior to extensive fire suppression. This includes determining when and where to allow wildfires to burn, building strategic fire guards and increased use of prescribed fire. The BVRC, in partnership with universities and other research agencies, provides scientific expertise and leads research project designed to ensure a science-based approach to the pilot project. Collaborations include projects to determine historic fire regimes - including the structure and composition at various scales, effectiveness of treatments such as prescribed fire in reducing forest flammability, meta-analysis of the effects of fire on ecosystem elements, and a study of the obstacles to making needed changes. The BVRC also provides extension services and hosts a variety of social media platforms, workshops and webinars and produces written material for the and practitioners to support the NWRI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".