Fire dynamics and implications for nitrogen cycling in boreal forests
Bibliographic record
Abstract
We used a dynamic, long‐term mass balance approach to track cumulative carbon (C) and nitrogen (N) losses to fire in boreal Manitoba over the 6500 years since deglaciation. Estimated C losses to decomposition and fire, combined with measurements of N pools in mature and burned forest floors, suggest that loss of N by combustion has likely resulted in a long‐term loss that exceeds the amount of N stored in soil today by 2 to 3 times. These estimates imply that biological N fixation rates could be as high as 5 to 10 times atmospheric deposition rates in boreal regions. At the site scale, the amount of N lost is due to N content of fuels, which varies by stand type and fire severity, which in turn vary with climate and fire dynamics. The interplay of fire frequency, fire severity, and N partitioning during regrowth are important for understanding rates and sustainability of nutrient and carbon cycling over millenia and over broad regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".