Baseline geographic information on wildfire-watershed risk in Canada: Needs, gaps, and opportunities
Bibliographic record
Abstract
As the pressures on water supply from shifting forest disturbance regimes continue to escalate, researchers are being asked to answer increasingly complex questions. However, many questions in wildfire-watershed risk (WWR) research remained unaddressed due to a paucity of relevant datasets. There are, indeed, many fundamental processes we do not understand that require additional data collection to develop risk management frameworks. As such, WWR researchers and managers face a paradox in their need to address critical questions important for the sustainability of socio-hydrological systems while dealing with incomplete information. In many cases, this leads to valuable research ideas being discarded on the account of limited data availability. However, imperfect, incomplete, or limited data should not deter researchers and managers from performing analyses to assess risk. In fact, such analyses improve the research benefit-to-cost ratio of existing data, help unravel gaps in data sources, enable generation of new hypotheses, and highlight where data availability and openness can be improved. If we do not use what we have, how can we know what we need? This issue is of particular interest in Canada, where baseline WWR information for the entire country is generally missing, despite growing concerns about water security in the face of a shifting wildfire regimes. In this commentary, we (a) identify several relevant open geospatial datasets, (b) illustrate how these datasets can be leveraged to produce simple yet relevant risk information, (c) identify some high priority data gaps that require immediate attention, and (d) discuss future avenues towards the creation of baseline Pan-Canadian WWR information.
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 | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".