A network of 18 wildfire chronosequences reveals key drivers of the boreal nitrogen balance
Bibliographic record
Abstract
Ecosystem productivity and carbon uptake in the circumpolar boreal forest are contingent on available nitrogen, which ultimately originates from inputs via deposition and biological nitrogen fixation. Nitrogen deposition rates in boreal forests are relatively small compared to other biomes, and most biological nitrogen fixation research has focused on moss-diazotroph associations. However, the relative contributions of these two primary nitrogen inputs to ecosystem nitrogen stocks have not been widely investigated. In this study, we combined a mass balance approach and literature synthesis to estimate rates of nitrogen accumulation and nitrogen inputs across a network of 18 wildfire chronosequences spanning the boreal biome. We found that nitrogen accumulation rates were strongly linked with fire regime (stand-replacing versus surface fires) and canopy dominance (deciduous versus evergreen canopies). Furthermore, a considerable amount of accumulating nitrogen in these boreal forests was unexplained by the known inputs estimated from the literature synthesis, particularly in forests with stand-replacing fire regimes and more deciduous tree cover that together had the highest nitrogen accumulation rates. This unexplained fraction of nitrogen inputs in some forests may originate from poorly quantified niches of biological nitrogen fixation. Exploring this research frontier will help improve predictions of boreal forest nitrogen cycling and carbon uptake in changing climate and wildfire regimes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".