The impacts of formative system on the landslides of Iran
Bibliographic record
Abstract
Landslide is one of the most challenging disasters on the earth, which is believed to cause other natural catastrophic incidents.Normally, in studying landslide we investigate different influencing factors such as gender land, atmospheric rainfall, gradients' change, earthquake, volcanic eruption, subterranean water vibration, and human causes in the form of different models.These facts are blamed as the main share in appearing this phenomenon.However, correlative and sufficient condition for genesis such a phenomenon is historical base of lands' bed, which needs specific formative process.There are several studies focused on distribution and dispersion of slides and their reasons.In this paper, we investigate the behavior of landslide and its effects on instigating instabilities.The preliminary results indicate that distribution of this phenomenon is associated with climate from a side and historical formative process on the other side.The weather condition of Iran is divided into four groups of cold, hot, humid and humid hot hole.Every region has its own special geomorphic properties and either directly or indirectly affects on landslide occurrence.In order to study this effect, we use Arc GIS 9.3 software dispersal map of Iran's main landslides and formative systems on the other side and by local analyzing these two collections are evaluated based on their vicinity relationship using localstatistical techniques.Results of this research shows that the main part of this landslide occurs in cold hole and humid hole and only about 8 percent are happens in hot holl.In addition, density of landslides are more in thermodynamic bound of cold and hot hole as well as cold and humid hole.
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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 | 0.001 | 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".