Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models
Bibliographic record
Abstract
Forest soils often contain more carbon (C) than living trees, with significant variation in soil organic carbon (SOC) stocks due to stand type and soil characteristics. This study evaluates SOC stocks in the Moroccan Middle Atlas forests using field measurements and machine learning models. Soil profiles across 16 forest types were analysed, identifying soil typology and measuring SOC stocks. Spatial variation in SOC stocks was influenced by stand type and substratum nature, as determined through supervised extrapolation analysis. SOC stocks ranged from 35 t SOC ha-1 on tree-free land to 252 t SOC ha-1 under mixed cedar (Cedrus atlantica) and zeen oak (Quercus canariensis) stands. To enhance estimation accuracy, Random Forest (RF) and Gradient Boosting Machine (GBM) models were tested. The GBM model outperformed the RF model, with an RMSE of 6.97 t C ha-1 and R2 of 0.99, compared to RF’s RMSE of 10.28 t C ha-1 and R2 of 0.44. For better SOC stock assessment in deeper soil layers, a strategy involving more surface soil samples (0–30 cm) combined with numerical modelling of proximal soil properties is recommended.
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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 | 0.000 | 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".