Gully erosion rate by means of anatomical changes in exposed roots tree rings in the Proboszczowicka Platou (southern Poland)
Bibliographic record
Abstract
Network of gullies can increase above 300 % during about 80 years (Swanson et all., 1989). More often the rate of gully erosion is 0,2-0,8 m/year (Martinez-Casasnovas, 2003). The analyze of precipitation, valley morphology and sites where erosion occurs can be use to building the local erosion models. The models allow estimate future gully erosion and very often we can estimate local thresholds to erosion occurs. One of the main factors conditioning gully erosion is the quantity and intensity of precipitation. In wet periods gully erosion may occur several times faster. However, studies conducted in Canada prove that natural processes associated with climatic fluctuations are insufficient to cause gully initiation, but may contribute to ongoing gully expansion. According to (Stankowianski, 2003) gullies are formed during periods of extensive forest clearance and expansion of farmland, but the triggering mechanism of gullying are extreme rainfall events. Local hill slopes gradient and drainage-basin area are the most important topographic parameters affecting gully erosion. Also lithological conditions as well as the thickness of dusty sediments and the underlying rock structure can be significant factors affecting the development of gully erosion. Studies of gully erosion velocity are often based on the comparison of gully lengths on maps produced in different centuries or on aerial photos (Burkard and Kostaschuk, 1995). Another method of measuring the velocity of gully erosion includes continuous monitoring of headcuts. Also a dendrochronological method has
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".