A geospatial dataset providing first-order indicators of wildfire risks to water supply in Canada and Alaska
Bibliographic record
Abstract
First-order, high level indicators of wildfire risk to water resources are paramount to understand growing wildfire-related water security challenges in Canada and Alaska. Information pertaining to forest cover, fire activity, water availability, and location of populated places was collected from multiple institutional sources. Manual and semi-automated processes were used to clean disparate source data and create four harmonized geospatial layers whose content was summarized for each of the 1468 existing sub-sub watersheds covering Alaska and Canada. The final dataset provides a master layer based on sub-sub-watershed boundaries that contains relevant information to create spatial indicators of wildfire risk to water security. These can be used to identify potentially at-risk regions in high-latitude watersheds of North America. The dataset can be further used within a larger, general risk assessment framework considering other environmental stressors to water security, including climate change and population growth. The dataset described herein was used to make a figure in the manuscript "Wildfire impacts on hydrologic ecosystem services in North American high-latitude forests: A scoping review" by Robinne et al. [1].
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".