Relational Modelling of the Earth's Surface Topography Impact on Vegetation Density Using RS and GIS: Rawnduz as a Model
Bibliographic record
Abstract
Through the analysis of the digital elevation model (DEM) of the search area, it was found that the search area is located within a mountainous region with a complex twisting, as the surface has been classified into five regions of elevations, among which the first region represents the lowest elevation lands, and extends an area of 118.7 km2 It equates to (22.6%) of the total area, while the largest region is the second region, occupying an area of (187.9) km2, 36% of the total area of the region, while the regression categories were divided into five levels depending on the classification of (Zink) It turns out that the Fifth Region is the most complex of the regions, and it includes the summit of Mount Hendrin, the summit of Mount Karukh. As for the characteristics of the direction of the slopes, nine slope directions of varying areas were found. As for the density of vegetation coverage according to (NDVI), we find that the NDVI index in the research area is divided into three levels of plant density, as the second level, i.e. average density, recorded the largest area at about (344.8) km2, equivalent to (66.1%) of the area the college. Which is characterized by the topological complexity of the surface, which makes it the most suitable areas for pastoral activity, while the higher density in relation to vegetation coverage was more widespread in the first and second steep categories by about (2.5, 9.8) km2, i.e. (21.6%, 24.3%) of the total area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".