The influence of seasonal vertical temperature gradients on no‐purge sampling of wells
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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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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)
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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 it