Influence of species identity and charring conditions on fire-derived charcoal traits
Bibliographic record
Abstract
Fire is a major disturbance worldwide, and it produces significant amounts of wood-derived charcoal. There is increasing evidence that the key properties (“traits”) of charcoal vary greatly, with consequences for ecosystem processes, but how the key factors drive variability of charcoal traits, i.e., species identity and charring conditions, remain poorly understood. Here, we experimentally produced charcoal from three common boreal tree species under six charring conditions representing those encountered during boreal fires and then analyzed their structural and chemical traits. Overall, we found that species identity affected charcoal traits more than did charring conditions. Among the structural traits, density and microporosity varied among tree species, and density decreased with increasing temperature. Among the chemical traits, electrical conductivity, total nitrogen (N) and phosphorus (P) contents, and phosphate concentration differed among species, whereas pH, total N content, and ammonium concentration responded to charring conditions. No traits except nitrate concentration responded to the interactive effect of species identity and charring condition. Our results reveal that traits of charcoal, and potentially its ecological functions, are driven by a combination of fire behavior and tree species identity; such information is relevant for understanding ecological consequences of altered fire regimes due to the changing climate and to forest management.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".