Bibliographic record
Abstract
history with $2.8 Billion (USD) in insured losses and at least the same in indirect costs. The province of British Columbia broke records in 2017 and again in 2018 for total burned area of over a million hectares each year. It is currently estimated that by year 2100 the average annual national burned area could double the current 2.4 million hectares. Experts worry it is only a matter of time before Canada also loses lives to wildfire.Historically Canada was a world leader in wildfire science and systems, but over the past couple decades research investments have not kept pace with the evolving complexity of wildfire and the contributing risks. In 2016 the Canadian Council of Forest Ministers endorsed a renewal of the 2005 Canadian Wildland Fire Strategy, which emphasizes the original goals: to develop resilient communities, ensure healthy forest ecosystems, and modernize business practises. The recent endorsement highlighted the need for multi-agency collaboration and the urgency to develop new approaches to these emerging challenges.The Canadian Forest Service led the development of The Blueprint for Wildland Fire Science in Canada to support a coordinated, multi-agency approach and build a clear business case for investment. A series of consultations collected input from over 100 individuals and organizations across the country. The broad cross sectional participation provides confidence the Blueprint is a representative summary of wildfire in Canada.The Blueprint defines six themes and 15 key recommendations intended to guide a 10 year research strategy in Canada to address the identified knowledge gaps. The results of this work will be applicable in both Canadian and international contexts and support inter-jurisdictional collaborations. This talk will present the themes and recommendations in detail.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".