The influence of seasonal vertical temperature gradients on no‐purge sampling of wells
Bibliographic record
Abstract
Abstract Seasonal changes in ambient temperature create vertical temperature gradients in shallow groundwater (less than 15 m). These temperature gradients can affect in‐well flow dynamics that impact samples collected using no‐purge sampling methods. In late winter, the shallower water is colder, resulting in thermally mixed conditions and uniform contaminant concentrations. In late summer, the shallower water is warmer, resulting in thermally stratified conditions and contaminant distributions in the monitoring well more consistent with the distribution in the surrounding aquifer. The importance of seasonal temperature gradients on in‐well mixing was evaluated in two shallow monitoring wells in Houston, Texas. In each of the two wells, four vertically spaced passive diffusion samples collected in late winter showed a less than 1.3x difference in trichloroethene (TCE) concentration between depths, while the same sampling conducted in late summer showed greater than a 100x difference in TCE concentration between depths. A simple analytical model originally developed to predict vertical soil temperature profiles can also be used to predict the occurrence of thermally stratified and thermally mixed conditions in monitoring wells as a function of time and well depth. The results of this analysis and modeling suggest that shallow monitoring wells in most of the United States and Canada can have significantly different vertical concentration profiles within the well over the course of a year due to seasonal vertical temperature gradients. This can induce additional intra‐annual temporal variability on passive no‐purge sampling results from these shallow wells, potentially making it more difficult to discern true trends in the data. © 2012 Wiley Periodicals, Inc.
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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 | 0.002 |
| 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) | 0.001 | 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".