The effect of litter addition on soil microbial and enzyme indices after forest harvesting operations in Hyrcanian deciduous forests, twenty years trajectory
Bibliographic record
Abstract
Abstract Improvement of soil characteristics in natural conditions after ground-based forest harvesting operations is necessary to maintain soil quality. Therefore, the present study was conducted to examine the effect of different tree litters on soil microbial properties and enzyme activity of skid trails over a 20-year period after skidding operations. A total of 216 soil samples were taken from three litter treatments (B; beech, B-H; beech-hornbeam, B-H-O; mixed beech) exposed to three traffic intensity classes (low, medium, and high) that were each assigned to three time periods (6, 10 and 20 years after harvesting). All combinations were replicated three times. The highest value of soil microbial characteristics and enzyme activity belonged to B-H-O treatment followed by > B-H > B and 20-years since harvest followed by > 10 years > 6 years. Recovery of microbial properties and enzyme activities under the influence of high-quality litter (mixed beech) was positively associated with other soil properties including pH, total N, available nutrients. Values of soil microbial properties including soil microbial respiration (15%), microbial biomass carbon (8.7 %), microbial biomass nitrogen (15.4%), NH4+ (13.6%), NO3- (9.8 %) and enzyme activities such as urease (10.2%), acid phosphatase (4.4%), arylsulfatase (8.8%), invertase (6.9%) in the B-H-O treatment measured 20 years after harvesting were less than the values of the undisturbed area. According to these results, it seems that the mixed beech litter treatment has been able to improve soil properties more than the other tested treatments.
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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".