Soil nitrogen dynamics three years after a severe <i>Araucaria–Nothofagus</i> forest fire
Why this work is in the frame
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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 13 C‐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 (COREC arom ), a relative measure for char. The LF fraction, a strong NH 4 + sink, decreased as a result of fire, while COREC arom increased in the burned soil profile and stimulated NO 3 ‐ 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.005 | 0.005 |
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 it