Estimation of Aquifer Transmissivity From Analysis of Long‐Term Monitoring With the Thiem Solution
Bibliographic record
Abstract
The application of the Thiem equation to support the interpretation of comprehensive long-term monitoring datasets, made possible through modern datalogging technology, is presented as an alternative to constant-rate aquifer testing to obtain representative transmissivity estimates in settings where controlled hydraulic testing may be impractical. Water levels logged at regular intervals can be readily converted to average water levels over time periods corresponding to periods of known pumping rates. By regressing average water levels during multiple time periods of known but variable withdrawal rates, steady-state conditions can be approximated and Thiem's solution applied to estimate transmissivity, without performance of a constant-rate aquifer test. Although the application is limited to settings where changes in aquifer storage are negligible, by regressing long data sets to parse interferences the method may characterize aquifer conditions over a much wider radius than short-term, non-equilibrium tests. As with all aquifer testing, informed interpretation is critical to identifying and resolving aquifer heterogeneities and interferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".