Investigating Groundwater Resources using Vertical Electrical Sounding: A Case Study in Baidoa, Somalia.
Bibliographic record
Abstract
Baidoa or Baydhabo, as is locally known, is the Bay region's capital, a strategic town in south-central Somalia situated approximately 250 kilometers west of Mogadishu and 240 km southeast of the Ethiopian border. The town is divided into four quarters, namely Isha, Berdaale, Horseed and, Howl Wadaag. Each quarter is further divided into six sections. The city is traditionally one of the most important economic centers in southern Somalia, conducting significant trade in local and imported cereals, livestock and non-food items (Eno et. Al, 2021). Similarly, the above study discusses, the combined effects of drought and the ongoing crisis in Baidoa have hurt economic stability and livelihoods, leading to a chronic humanitarian situation and major population displacements. The investigation area named Boonkaay, Ali amxar, Bayaxaaw, Abowasharow, and Mayfuulka Villages, it’s situated around 5 km2 of Baidoa, and the Accessibility of the road's general is good. The following coordinates can identify the area: latitude 3.11 and longitude. 43.65. Its elevation is 445. Therefore in this study, I will conduct a Geophysical Investigation in the Baidoa district at these Villages south west State of Somalia. Fieldwork will be carried out from August to December 2023. The state of knowledge about hydrogeology, quality, and quantity of groundwater resources is very poor in the target areas. Information on hydrogeology to facilitate drilling and development of strategic water sources is limited, and scattered, and in some cases, it doesn’t even exist. However, in many cases, groundwater drilling projects in the target areas are unguided and exploration takes place without investigations leading to low success rates and, thus wastage of financial resources (Wairia, 2023).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".