Spatial distribution and controlling factors of snow avalanche and debris flow in Parâng Mountains
Bibliographic record
Abstract
The alpine slopes of Parâng Mts. are prone to snow avalanche (SA) and debris flow (DF) activity due to favorable lithological, topo-climatic and vegetation cover conditions.Since tourism activities are continuously increasing, tourists and related infrastructure in this area might be exposed to different levels of risk.Our study is a preliminary research that aims to analyze the spatial distribution and controlling factors of SA and DF processes in Parâng Mts., which represent natural hazards threating the areas destined for tourism activities and associated infrastructure.First, the spatial distribution pattern of SA paths and DF tracks was identified and mapped on orthophotoplans, then checked in the field and integrated in a database for analysis.Subsequently, a set of instability factors influencing directly the spatial distribution of SA and DF activity were analyzed.The following instability factors were taken into consideration and statistically analyzed using ArcGIS software: slope, altitude, aspect, planar and profile curvature (automatically extracted from 10 m resolution DEM), vegetation cover (mapped on orthophotoplans) and lithology (extracted from the geological maps at 1:200.000 scale).Information regarding the spatial distribution of tourist frequented areas and the related infrastructure (hiking trails, chalets, ski lifts, ski pistes etc.) were analyzed in conjunction with the spatial distribution of geomorphic process activity.In areas with tourism activities, future research will focus on mapping terrain susceptibility to SA and DF processes.Moreover, dendrogeomorphic reconstructions will allow to determine the occurrence probability of SA and DF activity, which will serve to finally get an accurate geomorphic hazard zonation within the study area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".