Soil quality assessment of vegetation restoration after a large forest fire in Daxing’anling, northeast China
Bibliographic record
Abstract
Forest fires significantly change soil function and quality. Finding an effective way to accelerate the restoration of soil quality after forest fires is a major issue. This study investigated the soil quality index (SQI) during vegetation restoration after a fire in a Larix gmelinii plantation, throughout different restoration years. Fifteen physical, chemical, and biological properties of soil were examined using principal component analysis, and soil quality was assessed by SQI. The results revealed that soil physical properties, chemical properties, and enzyme activities showed the most improvement after 24 yr of restoration. Soil microbial biomass carbon content and microbial biomass nitrogen content increased with restoration years. Soil bulk density, microbial biomass nitrogen, sucrase, and catalase were retained in the minimum data set. After 24 yr of restoration, SQI was the highest, with a value of 0.52, followed by natural restoration, 21, 13, and 16 yr; the SQI after 3 yr of restoration was the lowest, with a value of 0.26. Artificial regeneration accelerated the recovery of soil quality after 24 yr. Soil quality should be monitored continuously in the study area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".