Impacts of fire and fire surrogate treatments on ecosystem nitrogen storage patterns: similarities and differences between forests of eastern and western North America
Bibliographic record
Abstract
The Fire and Fire Surrogates (FFS) network is composed of 12 forest sites that span the continental United States, all of which historically had frequent low-severity fire. The goal of the FFS study was to assess the efficacy of three management treatments (prescribed fire, mechanical thinning, and their combination) in reducing wildfire hazard and increasing ecosystem sustainability. This paper describes the impact of the FFS treatments on nitrogen (N) storage and distribution. At the network scale, total ecosystem N averaged 4480 kg·ha–1, with ∼9% in vegetation, ∼9% in forest floor, ∼2% in deadwood, and ∼80% in soil. The loss of vegetation N to fire averaged (±SE) 25 ± 11 kg·ha–1, whereas the mechanical and combined mechanical and fire treatments resulted in N losses of 133 ± 21 and 145 ± 19 kg·ha–1, respectively. Western coniferous forests lost more N from each treatment than did eastern forests. None of the manipulative FFS treatments impacted >10%–15% of total N of these ecosystems. Management strategies that maximize ecosystem carbon (C) gain by minimizing loss of N should be a focus in western forests, where C and N cycling are tightly linked, but perhaps not in those eastern forests where atmospheric N deposition has decoupled C and N cycles.
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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".