Effects of root tensile force and diameter distribution variability on root reinforcement in the Swiss and Italian Alps
Bibliographic record
Abstract
Root reinforcement is considered to be one of the most important factors contributing to the stability of vegetated hillslopes; however, its estimation is still challenging because of the spatial variability of root distribution and root mechanical properties. This work uses the root bundle model to assess the sensitivity of root-reinforcement estimation to the variability in both root mechanical properties and root distribution. We used a large data set of root tensile tests and root distributions of an important alpine species, Picea abies (L.) Karst., collected in a wide range of altitudinal and climatic ranges on both north and south sides of the alpine mountain range. The results demonstrate that the site-specific characterization of mechanical properties and root distribution is fundamental for the quantification of root reinforcement at the stand scale. Root diameter distribution plays a dominant role in influencing the root-reinforcement model’s output; however, in contrast with results from other works, differences in root diameter–force functions are significant and cannot be ignored. Model results also show that coarse roots contribute significantly more to the reinforcement of soil than fine roots, underlying the need of additional data for roots with diameters larger than 5 mm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".