Soil nitrogen dynamics three years after a severe <i>Araucaria–Nothofagus</i> forest fire
Bibliographic record
Abstract
Abstract Wildfires have shaped the biogeography of south Chilean Araucaria–Nothofagus rainforest vegetation patterns, but their impact on soil properties and associated nutrient cycling remains unclear. Nitrogen (N) availability shows a site‐specific response to wildfire events indicating the need for an increased understanding of underlying mechanisms that drive changes in soil N cycling. In this study, we selected unburned and burned sites in a large area of the National Park Tolhuaca that was affected by a stand‐replacing wildfire in February 2002. We conducted net N cycling flux measurements (net ammonification, net nitrification and net N mineralization assays) on soils sampled 3 years after fire. In addition, samples were physically fractionated and natural abundance of C and N, and 13C‐NMR analyses were performed. Results indicated that standing inorganic N pools were greater in the burned soil, but that no main differences in net N cycling fluxes were observed between unburned and burned sites. In both sites, net ammonification and net nitrification fluxes were low or negative, indicating N immobilization. Multiple linear regression analyses indicated that soil N cycling could largely be explained by two parameters: light fraction (LF) soil organic matter N content and aromatic Chemical Oxidation Resistant Carbon (CORECarom), a relative measure for char. The LF fraction, a strong NH4+ sink, decreased as a result of fire, while CORECarom increased in the burned soil profile and stimulated NO3‐ production. The absence of increased total net nitrification might relate to a decrease in heterotrophic nitrification after wildfire. We conclude that (i) wildfire induced a shift in N transformation pathways, but not in total net N mineralization, and (ii) stable isotope measurements are a useful tool to assess post‐fire soil organic matter dynamics.
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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 | 0.000 | 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".