Do changes in soil properties after rooting by wild boars (<i>Sus scrofa</i>) affect understory vegetation in Swiss hardwood forests?
Bibliographic record
Abstract
Recovering from small fragmented populations, wild boars ( Sus scrofa L.) have considerably increased their numbers and their habitat range in many European countries during the past two decades. Although several studies have focused on the impact of wild boar rooting on selected vegetation properties, little is known about effects on entire forest ecosystems. The main goal of our study was to assess how rooting by boars alters soil and vegetation properties. We measured soil chemical and biological properties (C and N concentrations, N availability, and microbial biomass C) as well as several vegetation characteristics (total plant cover, plant species diversity, and number and height of saplings) on paired rooted and non-rooted plots in six hardwood forests in Switzerland. We found that rooting by wild boars led to significant increases in mineral soil C and N concentrations and microbial biomass C, which could lead to improved growth conditions for plants. However, total plant cover and sapling counts were reduced on rooted plots, possibly due to mechanical disturbance or due to reduced plant available N (measured as supply rate in contrast with the observed increase in total stocks of mineral soil N). In view of these results, simple characterizations of wild boar rooting as beneficial or detrimental to forest ecosystems should be handled with care.
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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) | 0.001 | 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".