The Gavkhouni Wetland Dryness and Its Impact on Air Temperature Variability in the Eastern Part of the Zayandeh-Rud River Basin, Iran
Bibliographic record
Abstract
The Gavkhouni wetland provides many environmental and economic benefits for the central region of Iran. In recent decades, it has completely dried up several times with substantial impacts on local ecosystems and climate. Remote sensing-based Land Surface Temperature (LST), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) in combination with in-situ data were used to investigate the trend of the Gavkhouni wetland dryness and the associated impact on the variability of local air temperature. The results indicate that the wetland has increasingly experienced drier conditions since the year 2000. The wetland was almost completely dry in 2009, 2011, 2015 and 2017. In addition, the results show that Gavkhouni wetland dryness has a significant impact on local climate, increasing the mean seasonal air temperature by ~1.6 °C and ~1 °C in spring and summer, respectively. Overall, this study shows that remote sensing imagery is a valuable source for monitoring dryness and air temperature variations in the region. Moreover, the results provide a basis for effective water allocation decisions to maintain the hydrological and ecological functionality of the Gavkhouni wetland. Considering that many factors such as latitude, cloud cover, and the direction of prevailing winds affect land surface and air temperatures, it is suggested to use a numerical climate model to improve a regional understanding of the effects of wetland dryness on the surrounding climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".