Water Resources Improvement in Southeast Afghanistan: Remote Project Planning and Decision Support Modeling
Bibliographic record
Abstract
Improved use of water resources in Afghanistan has become an important priority in U. S. Army operations. To further this, the Army Corps of Engineers, the U. S. Dept. of Agriculture, and the U. S. Geological Survey in conjunction with the U. S. Army's Task Force (TF)Yukon (4th Brigade Combat Team (Airborne), 25th Infantry Division) has undertaken a ground-breaking project to assess and prioritize numerous water resource improvement projects in SE Afghanistan. Conditions for completing traditional site-related review and planning are, to say the least, difficult. Our objective here was to identify and evaluate potential water resource projects in the southeast provinces using applied remote sensing science and technology. Sources of data included high-resolution satellite imagery, high-resolution elevation models, ground-truth from field personnel, existing spatial data and reports, and authors' experience in-country. Thousands of square kilometers were reviewed at scales up to 1:750 to identify and evaluate 295 potential water resource projects (storage dams, diversions, power generation, or upgrades to existing facilities, and watershed restoration). Each project was then prioritized using an industry-standard decision support model, integrating both engineering and watershed factors, such as sedimentation and stream system stability, cost indices, storage efficiency, benefiting agricultural lands, and environmental impacts. This study provided a systematic, detailed product to support field design, based on recognized expert evaluation. The report is now providing guidance to the Afghan ministries and coalition partners on developing water resource projects in a responsible and sustainable manner. In addition, the collected data are being used to scope potential water restoration projects and develop site plans. Utilizing remote sensing technology and expert personnel in this manner helped to maximize effectiveness of field investigation, sending ground personnel to only the most favorable sites for further evaluation, thereby reducing time on the ground in a difficult environment.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".