Charcoal production, dispersal, and deposition from the Fort Providence experimental fire: interpreting fire regimes from charcoal records in boreal forests
Bibliographic record
Abstract
The relationship between charcoal production from fires and charcoal deposition in lakes is poorly understood, which limits the interpretation of sediment charcoal records. This calibration study assessed charcoal particle production, size, and transport during the International Crown Fire Modelling Experiment (ICFME) and compared fossil charcoal particle accumulation from 16 lakes in boreal forests of North America. Particle accumulation averaged 20.1 mm 2 ·cm 2 inside the ICFME fire; accumulation declined sharply outside the fire, with only 1% of the measured particles transported beyond 20 m from the burn edge. Fossil charcoal accumulation during the past 9000 years was much lower than observed deposition in traps located within the ICFME fire but similar to airborne deposition in traps located 1060 m from the burn edge. A higher fraction of large diameter particles (>1 mm) was present in fossil charcoal accumulation from historical fires and charcoal peaks that exceeded background accumulation by 1.4 times, suggesting large particles are characteristic of nearby fires. On the basis of a charred-particle production of ~2% of the total fuel consumed by the ICFME fire, we estimate a potential long-term carbon sequestration of 58.2 ± 12 g C·m 2 as charred particles from this fire stored in soils or lake sediments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".