Using Heat to Trace Vertical Water Fluxes in Sediment Experiencing Concurrent Tidal Pumping and Groundwater Discharge
Bibliographic record
Abstract
Abstract Heat has been widely applied to trace groundwater‐surface water exchanges in inland environments, but it is infrequently applied in coastal sediment where head oscillations induce periodicity in water flux magnitude/direction and heat advection. This complicates interpretation of temperatures to estimate water fluxes. We investigate the convolution of thermal and hydraulic signals to assess the viability of using heat as a tracer in environments with tidal head oscillations superimposed on submarine groundwater discharge. We first generate sediment temperature and head time series for conditions ranging from no tide to mega‐tidal using a numerical model (SUTRA) forced with periodic temperature and tidal head signals. We then analyze these synthetic temperature time series using heat tracing software (VFLUX2 and 1DTempPro) to evaluate if conventional terrestrial approaches to infer fluxes from temperatures are applicable for coastal settings. We consider high‐frequency water flux variability within a tidal signal and averaged over tidal signals. Results show that VFLUX2 analytical methods reasonably estimated the mean discharge fluxes in most cases but could not reproduce the flux variability within tidal cycles. The model results further reveal that high‐frequency time series of water fluxes varying in magnitude and direction can be accurately estimated if paired temperatures and hydraulic heads are analyzed using numerical models (e.g., 1DTempPro) that consider both dynamic hydraulic gradients and thermal signals. These results point to the opportunity to incorporate pressure sensors within heat tracing instrumentation to better assess sub‐daily flux oscillations and associated reactive processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".