The Effect of Land Use Changes on Desertification Hazard (Case Study: Maraveh Tappeh Region of Golestan Province, Iran)
Bibliographic record
Abstract
Land degradation and desertification caused by land use change is mainly due to human activities in arid and semi-arid and affect on the sustainable use of lands. The aim of this study was to evaluate the effects of land use changes on the desertification hazard in Maraveh Tappeh region. In this research, land use maps of 1986, 2000 and 2014 is provided using images of MSS, ETM and OLI sensors of Landsat satellite and land use map of 2024 is predicted using Markov chain model. According to the results, dense forest area is decreased during study period and with passing time, the area of agricultural land has increased. The results for the time interval of 2014-2028, showed it is possible that will be decreased semi-dense forest and dense rangelands and will be increased other land-use areas according to results of model predictions. In the study years, desertification maps were prepared using ESAs method and with the assumption of fixed all factors other than land use factor. The results showed that there was a lack of desertification as a class in 1986, but this class has been removed in other study years and has been added the area of this class into other classes. The compare of desertification hazard classes and theirs percentage of area in studied time periods showed that critical (C3) and fragile (F3) classes had increasing trend, clearly. The difference between classes since 1986 to 2000 is clearer and more specific and destruction seem clearer.
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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 | 0.001 |
| 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".