Temporal relationships between agricultural and meteorological drought over the Oum Er Rbia River, Morrocco
Bibliographic record
Abstract
This study examines the temporal relationships between meteorological and agricultural drought indices using lagged and linear correlations, the Mann–Kendall trend test, and machine learning (random forest – RF and deep neural network – DNN). On a seasonal and annual scale, the results revealed that the resonance of agricultural drought is strongly synchronized with the temporal variability of meteorological drought. At the monthly scale, the resonance of agricultural drought reflected by the vegetation condition index and the soil moisture condition index (SMCI) has an obvious latency time of at least one month and is statistically significant up to three months. For both agricultural drought indices, their statistical relationships with meteorological drought indices are highly variable, depending on the month of the agricultural season, the time scale and the type of meteorological drought index. The correlations between the SMCI and Palmer drought severity index were the most stable. They ranged from 0.7 to 0.86, whereas the linear correlations between the SMCI and the precipitation conditions index varied from 0.5 to 0.16 in the first and last months of the agricultural season, respectively. Despite this high correlation variability, analysis of historical trends on an annual scale demonstrated the existence of obvious similarities of very negative trends in the spatiotemporal changes in agricultural and meteorological drought indices. Similarly, machine learning models highlighted the importance of the positive relative contribution of their joint occurrence to the annual variability in agricultural yields. Overall, the RF model achieved optimal performance with a relatively small number of predictors, whereas the DNN model was more dependent on the number of features used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".