Patterns of Ecosystem Structure and Wildfire Carbon Combustion Across Six Ecoregions of the North American Boreal Forest
Bibliographic record
Abstract
Increases in fire frequency, extent, and severity are expected to strongly impact the structure and function of boreal forest ecosystems. An important function of the boreal forest is its ability to sequester and store carbon (C). Increasing disturbance from wildfires, emitting large amounts of C to the atmosphere, may create a positive feedback to climate warming. Variation in ecosystem structure and function throughout the boreal forest are important for predicting the effects of climate warming and changing fire regimes on C dynamics. In this study, we compiled data on soil characteristics, stand structure, pre-fire C pools, C loss from fire, and the potential drivers of these C metrics from 527 sites distributed across six ecoregions of North America’s western boreal forests. We assessed structural and functional differences between these fire-prone ecoregions using data from 417 recently burned sites (2004-2015) and estimated ecoregion-specific relationships between soil characteristics and depth from 167 of these sites plus an additional 110 sites (27 burned, 83 unburned). We found that northern boreal ecoregions were generally older, stored and emitted proportionally more belowground than aboveground C and exhibited lower rates of C accumulation over time than southern ecoregions. We present ecoregion specific estimates of depth-wise soil characteristics that are important for predicting C combustion from fire. As climate continues to warm and disturbance from wildfires increases, the C dynamics of these fire-prone ecoregions are likely to change with significant implications for the global C cycle and its feedbacks to climate change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".