Bibliographic record
Abstract
Subarctic regions are undergoing rapid climate change, leading to increased permafrost thaw and more frequent wildfires. These environmental changes significantly impact water quality, which is critical for ecosystems, community health, and food security. Permafrost stores organic material, nutrients, and contaminants such as mercury. When thawed, these substances are released into surrounding water systems, enhancing hydrological connectivity and facilitating contaminant transport. Wildfires further alter hydrological pathways by burning vegetation and organic matter, increasing the release of nutrients and contaminants. Burnt landscapes can accelerate the production of methylmercury (MeHg), a highly toxic and bioaccumulative form of mercury that poses risks to aquatic and terrestrial food webs. This project examines how wildfire affects the production of methylmercury in boreal peatlands by comparing pre and post wildfire water quality data from Scotty Creek Research Center, Northwest Territories Canada. In 2022, the research station and surrounding area were affected by a devastating wildfire. Water quality data at sited examined by Gordon et al. in 2013 were revisited in 2024, allowing for direct comparisons of pH, temperature, dissolved organic carbon (DOC), total mercury, and MeHg concentrations. This study specifically investigates how MeHg concentrations have changed over time and how they correlate with other environmental variables. Additionally, it explores which landcover types exhibit the highest MeHg concentrations and the most significant post-fire changes. Preliminary results indicate a general increase in MeHg concentrations across all landcover types from 2013 to 2024. These findings provide critical insights into how wildfire-driven permafrost thaw influences contaminant dynamics in boreal peatlands. The results of this study will help inform water quality policies and mitigation strategies for managing post-wildfire water quality in northern regions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".