A New Statistic Approach towards Landslide Hazard Risk Assessment
Bibliographic record
Abstract
To quantitatively assess the landslide hazard in Khelvachauri, Georgia, the statistic method of hazard index was applied. A spatial database was constructed in Geographic Information System (GIS) including topographic data, geologic maps, land-use, and active landslide events (extracted from the landslide inventory). After that, causal factors of landslides (such as slope, aspect, lithology, geomorphology, land-use and soil depth) were produced to calculate the corresponding weights, and thereby we defined a relevant set of spatial criteria for the latter landslide hazard assessment. On top of that, susceptibility assessment was performed in order to classify the area to low, moderate and high susceptible regions. Results showed that NW aspect, mountain geomorphology, private land-use, laterite loam and clay, slope between 19 to 24 degrees, and soil depth between 10 - 20 cm were found to have the largest contribution to high landslide susceptibility. The high success rate (72.35%) was obtained using area under the curve from the landslide susceptibility map. Meanwhile, effect analysis was carried out to assess the accuracy of the landslide susceptibility, indicating that the factor of slope played the most important role in determining the occurring probability of landslide although it did not deviate as much as other factors. Finally, the vulnerability analyses were carried out by means of the Spatial Multi-Criteria Estimation model, which in turn, led to the risk assessment. It turned out that not so much of the number of buildings (~ 34.13%) was associated with high-risk zone and that governmental and private land-use almost accounted for the same risk (39.9% and 40.9%, respectively).
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".