Snow–avalanche history reconstructed with tree rings in Parâng Mountains (Southern Carpathians, Romania)
Bibliographic record
Abstract
Snow avalanches are a common phenomenon in Parâng Mountains (Southern Carpathians, Romania) perturbing tourism activities and associated infrastructures, damaging forests, and causing fatalities. Its past history is an essential information to gather while assessing the hazard zonation areas. Usually, in Romania snow–avalanche activity occurring in forested areas are neither monitored, nor recorded by historical archives. In these areas, environmental archives such as tree rings may provide useful information about the past avalanche activity. The purpose of the present study is to reconstruct snow–avalanche history with tree rings along a path located below Cârja Peak (2405 m a.s.l.), an area where past snow–avalanche activity still remains underestimated. In this sense, 57 Norway spruce (Picea abies (L.) Karst.) trees showing clear signs of disturbance by snow avalanches were sampled and the growth anomalies associated with the mechanical impact produced by snow avalanches on trees were identified within their rings and served to reconstruct past events. The reconstructed chronology covers the period 1994–2018 showing the occurrence of a minimum of 11 major events, with an average return period of 2.1 years. Tree–ring records provided the most consistent avalanche event chronology in the study area. Although the limited extension of the chronology back in time, a better understanding of snow–avalanche history which may be gained through dendrochronological reconstructions represent nonetheless useful and pertinent information to consider before the implementation and development of infrastructure in this mountain avalanche–prone 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".