Geopolitical Condition for Determinants of the Jordan Water Security
Bibliographic record
Abstract
This study aimed at studying the geopolitical condition for the determinants of the Jordan Water security through the following objectives: clearing the effective demographic data on Jordan water security. Disclosing the impact of the Syrian crisis on the Jordan Water Security and recognizing the influence of climate characteristics on Jordan Water Security and clearing the impact of the geopolitical condition on Jordan Water Security. The study had employed the analytical descriptive method…and it concerns with specifying reality and collecting facts about it and analyzing its sides, with what shares in working on developing it. The study deduced results meaning that the geopolitical condition played a role in the lack of water, because of the geographic nature of Jordan, the thing that led to fewness of water resources and increase of population and the flow of the Syrian refugees. It1 appeared clearly that the geopolitical condition plays a significant role in availability or fewness of water for the same source of water. The study deduced a recommendation meaning; working on benefiting from the Jordan geopolitical condition in exploiting the water harvest and the scientific method in water-storing, and the necessity of working with the international organization to guarantee non-influencing the Jordan geopolitical condition and agreement with the states of adjacency on water shares.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".