Assessing the Impacts of Climate Change Scenarios on Soil-Adjusted Vegetation Index in North African Arid Montane Rangeland: Case of Toujane Region
Bibliographic record
Abstract
Radiometric Vegetation Indexes are considered as good indicators of vegetation health and can contribute to explain its current and future evolutions. This study was carried out in the arid mountain rangeland of Toujane (southeast of Tunisia). Aims are about predicting how climate change will affect the Soil Adjusted Vegetation Index (SAVI) values under dryland conditions. Current and future SAVI indexes are analysed using maximum entropy algorithm (Maxent). The Canadian Earth System Model version 5 (CanESM5) represents the data source of two future climatic scenarios. These last, called Shared Socioeconomic Pathways (SSP245, SSP585), concern four time-periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100). Three topographic, twelve soil and nineteen climatic variables are undertaken during each time-period. Main results of the jackknife test showed that temperature, precipitation and some soil variables are the main factors influencing SAVI indexes for actual and future situations. The current findings can be used to understand how arid montane ecosystems may change in the future and help in exercising the suitable management accordingly.
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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) | 0.000 | 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 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".