PyTFLUX: An Analytical Framework for Quantifying Transient Vertical Groundwater Fluxes From Temperature
Bibliographic record
Abstract
Established closed-form analytical solutions for using heat as a tracer of vertical groundwater fluxes typically rely on assumptions of steady hydraulic conditions. We introduce a novel analytical approach and associated tool, PyTFLUX, to account for transient changes in vertical groundwater fluxes. The analytical solution uses a Fourier series to represent diurnal surface temperature variability and a differential method to represent vertical flux changes. Optimization techniques are employed to achieve faster convergence and prevent the estimation of unreasonable vertical fluxes. The PyTFLUX script, presented in a Python Jupyter notebook, enables the easy adoption of the new analytical framework. To test the new approach, illustrative transient vertical flux time series were developed for three time-varying groundwater flux scenarios: a step-change, a single sine-wave, and a mixed sine-wave. These profiles were analyzed to infer vertical groundwater flux time series using PyTFLUX and previously published methods implemented in VFLUX2. Results show that PyTFLUX can reproduce temporal variability in groundwater fluxes not typically captured by existing methods. Finally, previously published high-resolution sediment temperature data from the Quashnet River in Massachusetts, USA, were analyzed to demonstrate the efficacy of PyTFLUX in analyzing complex field data. The analysis of field data yielded a vertical flux time series with mean values that agreed with fluxes yielded from other approaches, but the new approach also revealed pronounced temporal flux variability that was obscured by other methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".