Natural recovery from fire disturbance is more favorable than assisted recovery for the restoration of soil nematode-trapping fungi
Bibliographic record
Abstract
A fire is usually considered a severe disturbance factor in the environment. Globally, rising temperatures and increasing human activities have intensified the severity and frequency of fire incidents, and research on post-fire recovery has inevitably become an important focus for ecologists. In terms of the restoration of burned areas, there are usually two primary approaches: natural recovery and assisted recovery. However, very few relevant studies have systematically compared these recovery alternatives, suggesting which one is more favorable to the overall restoration of an ecosystem, especially to the soil microbes that function as indispensable components of ecosystems. In this study, the restoration of soil nematode-trapping fungi (NTF) in natural and assisted recovery environments was compared. The results showed that although the NTF community structures differed among the sample sites, the counts and diversity of the NTF communities in the upper and lower soil layers in the natural recovery area were higher than those in the assisted recovery and unburned control areas. These findings suggest that artificial efforts to help ecosystem recovery after fire negatively affect the speed and quality of soil NTF community recovery. Instead, natural recovery appears to be a more suitable land management choice after fire disturbance.
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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 | 0.000 | 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".