Measuring Aquifer Specific Yields With Absolute Gravimetry: Result in the Choushui River Alluvial Fan and Mingchu Basin, Central Taiwan
Bibliographic record
Abstract
Abstract Accurate and densely covered specific yields (Sy) are essential for estimating the storage capacity of a groundwater reservoir. A cross‐well pumping test can determine Sy, but its high cost often makes it unsuitable for sampling high‐resolution Sy. The gravity‐based method (GBM) based on gravity changes near existing groundwater wells may outperform cross‐well pumping tests in estimating Sy. We established 10 gravity sites close to groundwater wells in the aquifer‐rich Choushui River Alluvial Fan and Mingchu Basin in central Taiwan and measured gravity changes with two FG5 gravimeters from 2012 to 2017. Thirty‐nine Sy values with formal errors are determined by natural rises and falls in gravity and groundwater level. The representative Sy values (0.04 to 0.29) from GBM are in general consistent with those from cross‐well pumping tests (0.03 to 0.24). Repeated groundwater level changes over similar depth range at different times serve as revisit tests, showing that GBM can reproduce a reliable Sy value at a given site and depth. Soil moisture and compaction data show that the effects of gravity changes originating from unsaturated zones and deep aquifer layers are minor. Using the cylinder model for aquifers with limited lateral extents, we assess the validity of the Bouguer model by quantifying gravity differences and relative Sy differences originating from the model assumption. Improvements in environmental resilience and transportability achieved by recent atomic gravimeters may increase the potential of GBM to replace or supplement cross‐well pumping tests in densifying Sy point densities for an improved groundwater resource management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".