REVIEW: Charcoal function and management in boreal ecosystems
Bibliographic record
Abstract
Summary Charcoal plays an important role in soil function and carbon storage in fire‐prone ecosystems. Charcoal is present in most boreal forest soils as a result of naturally recurring wildfires, which convert 0·7–2% of biomass to charcoal. In boreal forests, charcoal represents 8–10% of soil carbon and 1 pg of carbon globally. Charcoal is resistant to decay, representing a form of super‐passive carbon, with half‐lives one to two orders of magnitude greater than those of other soil organic matter. High concentrations of negative surface charges increase nutrient retention, impacting boreal soil function, productivity and species composition. Due to a lack of soil mixing processes, charcoal in boreal soils is vulnerable to recombustion in recurring fires, inhibiting the accumulation of charcoal over time, unlike in other fire‐prone ecosystems. Boreal charcoal stocks are highly variable. Increased fire intensity results in greater charcoal formation, with stand‐replacing crown fires resulting in much larger charcoal stocks than non‐stand‐replacing ground fires. Current estimates of carbon storage based on Scandinavian studies of non‐stand‐replacing fires may underestimate charcoal stocks by factors of 2–3. Synthesis and applications. Charcoal contributes to boreal soil function, ecosystem productivity, nutrient retention and carbon cycling. In the absence of fire, charcoal loses many active properties, contributing to declining productivity with increasing time since fire. Incorporation of charcoal into ecosystem management using prescribed burns may contribute to sustainable management of boreal forests and maintaining global carbon cycles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".