Contribution to the Modeling of the Organic Matter of Moroccan Forest Soils within the Context of Global Change: Case study of the Central Plateau
Bibliographic record
Abstract
Organic matter is a major component of soil.It is of considerable ecological importance given its role in determining soil health, influencing ecosystem productivity and climate.For this reason, it is essential to carry out studies to evaluate its dynamics in natural ecosystems.In this study, the authors aimed to explore the dynamics of soil organic matter (SOM) in forest ecosystems of the Central Plateau in Morocco, as well as to investigate the potential of spectral vegetation indices in modeling SOM.To this end, the soil samples for analysis were collected from 30 sites across three vegetation types, including cork oak, Barbary thuja and scrub (matorral).In addition, the normalized difference vegetation index (NDVI) was extracted from Landsat 8 images to be used to model SOM using linear regression.The obtained results showed a weak, although statistically significant (α < 0.05), correlation between NDVI and SOM at 0.45.In addition, only the scrub type showed a statistically significant (α < 0.05) relationship between its corresponding SOM and NDVI, and was therefore retained for modeling.Vegetation type had a statistically strong influence (α < 0.01) on SOM, with cork oak and garrigue ecosystems having the highest and lowest SOM contents with 5.61% and 2.36%, respectively.In addition, the highest SOM contents were observed under slightly acidic pH soils on mild, warm slopes at high altitude sites, while the lowest were found in lowland areas with predominantly weakly evolved soil.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".