Image_2_Assessing the broadscale effects of wildfire under extreme drought conditions to boreal peatlands.TIF
Bibliographic record
Abstract
<p>Climate warming and changing fire regimes in the North American boreal zone have the capacity to alter the hydrology and ecology of the landscape with long term consequences to peatland ecosystems and their traditional role as carbon sinks. It is important to understand how peatlands are affected by wildfire in relation to both extent of burn and severity of burn to the organic soil (peat) layers where most of the C is stored. Peatlands cover more than 75% of the landscape in the southern Northwest Territories, Canada where extreme drought led to widespread wildfires in 2014–2015. To assess the wildfire effects across a 14.6 million ha study area including 136 wildfire events, we used an integration of field data collection, land cover mapping of peatland and upland ecotypes, Landsat-8-based mapping of burn severity to the soil organic layers, and MODIS-hotspot mapping of fire progression for season of burning. The intersection of these geospatial products allows for a broadscale assessment of wildfire effects across gradients of ecotype, ecoregions, seasons, and year of burn. Using a series of chi-squared goodness of fit tests, we found that peatlands are more susceptible to wildfire on the Taiga shield where they are smaller and hydrologically isolated by the rocky landscape. There burning affected proportionally larger peat areas with an evenness of burn severity to the organic soil layers which may lead to less spatial diversity in post-fire recovery, making the landscape less resilient to future fire. The most resilient peatlands are expected to be hydrologically well-connected to both ground water systems and larger peatland complexes such as those on the Taiga plains which exhibited large unburned and singed patches across the landscape, and greater variability in burn severity across seasons and ecotypes. Understanding the tipping point of drought conditions at which the landscape becomes connected, and peatlands are susceptible to wildfire with deeper burning of the organic soil layers is important for understanding the potential future effects of climate change and projected increases in wildfire on peatlands. This is critical for C accounting and climate mitigation strategies.</p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.497 | 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".