Technique of the rapid detection of groundwater based on multidimensional space
Bibliographic record
Abstract
Abstract The ability to detect groundwater quickly and accurately is critical to the work of drought resistance. However, conventional groundwater detection methods are inefficient and costly. To solve the difficulties of using water in water-deficient areas, the method of rapid groundwater detection based on multidimensional space was presented. First, using satellite remote sensing (RS) technology, factors related to the occurrence of groundwater, such as lithology, structure, and landforms, were obtained. Then, through quantitative inversion of aeromagnetic data, the lithology and structure were improved. Using geographic information system (GIS) as an information analysis platform, the water storage conditions of various lithologies, structures, and landforms were comprehensively studied; and a water-rich target area was delineated. Based on the above-mentioned achievements, the ground geophysical prospecting work was carried out, the optimum well-position determined, and the target area accuracy verified by drilling data. The method integrates many technical means, such as satellite RS, airborne RS, ground physical exploration, and exploration drilling to detect groundwater, incorporating the advantages of each method. Through the preliminary application in the city of Beipiao, China, the well completion rate is 72.73%. High-quality groundwater resources were exploited in this area, proving it to be an effective method for accurately detecting groundwater.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".