Correlation of Surface Geophysical and Logging Data of Some Selected Boreholes in Imo State, South Eastern Nigeria
Bibliographic record
Abstract
Boreholes have become a major source of water supply in South Eastern Nigeria, an area with diversity in geology, topography and climatic conditions. The common approaches in borehole investigations are surface survey and logging. The two approaches are supposed to be complementary. However, for small schemes of groundwater development, logging is hardly considered.. Six boreholes were selected from locations at the three geographical zones of Imo state. The selected locations are Umueze, Umuduru, Ogbor-Ugiiri, Ngor Okpuala and Eziama. Geophysical survey and logging were carried out. Electrical resistivity method was adapted for geophysical survey. ABEM Terrameter (SAS) 300B with digital read-out was used for logging. The degree of correlation between the variables was determined by computing the coefficient of correlation denoted as R2. The results indicate generally poor correlations between logging and geophysical surface values for the selected boreholes except for the one located at Ngor Okpala with R2 value of 0.7408. At this location, geophysical surface method for borehole locations can be carried without any Logging exercise to establish the Total Drilling Depth (TDD). This will help reduce the total cost for the drilling of the boreholes and also save time and much desired energy. In those areas where no correlation exists, there is need for Government Financial support in drilling sustainable borehloes as much costs are involved in investigation and construction works.
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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 | 0.001 |
| 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".