Consumption of residual pyrogenic carbon by wildfire
Bibliographic record
Abstract
Pyrogenic carbon (PyC) produced during vegetation fires represents one of the most degradation resistant organic carbon pools and has important implications for the global carbon cycle. Its long-term fate in the environment and the processes leading to its degradation are the subject of much debate. Its consumption in subsequent fires is considered a potential major mechanism of abiotic PyC degradation; however, no quantitative data supporting this removal pathway have been published to date. To address this gap, we quantified consumption of residual PyC at the forest floor during an experimental fire, representative of a typical boreal wildfire, complemented by exploratory laboratory heating experiments. Labelled PyC (pinewood charcoal from a slash pile burn), in granular form contained in stainless steel mesh bags and as individual pieces, were placed at ~2-cm depth within the forest floor. The median mass loss of granular charcoal was 6.6%, with 75% of the samples losing <15%, and of individual pieces 15.1% with 75% of the samples losing <25%. The mass losses under laboratory conditions, although somewhat higher than in the field, confirm an overall low consumption of PyC. The limited losses of PyC found here do not support the widely held notion that wildfire is a major cause of loss for residual PyC.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".