Weather Parameter Trends and Variability of the Water Balance in Constantine State, Algeria
Bibliographic record
Abstract
This article aims at investigating the weather parameter trends impact on the production of drinking water and water balance in the state of Constantine, Algeria. In this respect, statistical criteria (the Mann-Kendall trend test, linear regression model and deviations from the average) were used to show the close relationship between the general trend and the variability of the annual mean temperature and rainfall during the period from 1976 to 2020, as well as the variability in the waterbalance to determine the water gap for the period 2007-2019. The results from the Mann-Kendall test shows that the annual trends of the minimum, maximum and normal temperature are increasing after showing positive Mann-Kendall Z-values (0,41; 5,29 and 3,74) respectively. However, the annual rainfall trend was observed to be decreasing after having a negative Z-value (-1,36). Therefore, the estimation parameters of linear regression correlate with the study results. However, an examination of the12- year (2007-2019) water balance revealed it is evident that water production is not significantly affected by the general trends of temperature and rainfall. Additionally, the distribution of drinking water reveals an inequality, and real needs of drinking water in this state are not under control.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".