Improving the spatial density of a regional hydraulic conductivity dataset with estimates made from domestic water well information
Bibliographic record
Abstract
High-quality hydraulic conductivity data tend to be sparse relative to the large areas under investigation in regional-scale groundwater studies. From the 1960s through the 1990s, the literature is replete with articles discussing the details of augmenting high-quality hydraulic conductivity datasets with values estimated from specific capacity tests. Early publications focused on the development of the fundamental mathematical relation between specific capacity and hydraulic conductivity. Later articles presented approaches claiming improved accuracy of hydraulic conductivity estimates from specific capacity by correcting for additional well losses. However, because the site-specific data required to correct for additional well losses are generally not available, these corrections often led to error. Through the 1990s, many workers abandoned the analytical approach for an empirical one, arguing that correcting for additional well losses introduces significant error into the K estimate. Since the early 2000s, the literature has become largely silent on the topic of augmenting high-quality hydraulic conductivity datasets with K estimates developed from specific capacity. Despite a pause in the literature, integration of the results of high-quality tests within more approximate but spatially extensive datasets is still desired, particularly to support the identification of groundwater resource exploration targets in complex, heterogeneous geological settings. We present a simple and effective approach to synthesize the results from high-quality hydraulic tests with reconnaissance-level hydraulic conductivity estimates made from domestic water well specific capacity tests. Domestic water wells are ubiquitous across Ontario, and although their associated well records do not contain the information required to support rigorous hydraulic test analyses, they do contain the information required to calculate specific capacity. We use the fundamental Theis relation to make hydraulic conductivity estimates from specific capacity tests, assuming that additional well losses account for a relatively small portion of the total observed drawdown. The utility of this approach is demonstrated with data comparisons at three different spatial scales. Each comparison is conducted to evaluate the suitability of augmenting the high-quality data set with lesser quality estimates to answer specific research questions. The results of the comparisons results show good correlation between both sets of hydraulic conductivity estimates, and provide the information required to support careful data integration for improved hydraulic conductivity data coverage.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".