Bibliographic record
Abstract
Awareness of flood zone is widely used in studies of river management, safety of beaches and environmental issues.Given the importance of this issue, determining flood zones with different return periods are necessary to identify high risk areas for flood insurance, create user mandatory limits in high risk areas, avoid risk of floods, organize and optimize rivers and determine available facilities in adjacent of rivers.The research estimates flood zones and economic losses caused by Karun River in Khuzestan province by integrating hydraulic model of HEC-RAS using GIS software through HEC-GeoRAS annexation.To determine flood zone, there is firstly created geometry file of the studied river in GIS environment using HEC-GeoRAS side-program.Then it is transmitted to HEC-RAS model and GIS of extending flood zone in return periods of 25, 50, 100, and 200 years will be evaluated after calculating the required parameters and sending results to the environment.In this method, it is necessary to have full topographic data of the region and hydraulic conditions along the river, in order to calculate flood zoning.Using the data, there have been determined flood zones for different levels in different return periods. .On the return periods of 100, and 200years across the distance of 2500 meters from the beginning of the area for 800 meters length and across the distance of 5380 meters from the beginning of the area for 1250meters length off the right bank, and the lengths of 2500 meters to 3300 meters, and the lengths of 7940 meters to 8730 meters off the left bank of the river, the water advances in to the farms, and residential areas, and, according to the findings, the torrent flood over higher return periods
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.967 | 0.970 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".