Interpreting Repeated Temperature‐Depth Profiles for Groundwater Flow
Bibliographic record
Abstract
Abstract Temperature can be used to trace groundwater flows due to thermal disturbances of subsurface advection. Prior hydrogeological studies that have used temperature‐depth profiles to estimate vertical groundwater fluxes have either ignored the influence of climate change by employing steady‐state analytical solutions or applied transient techniques to study temperature‐depth profiles recorded at only a single point in time. Transient analyses of a single profile are predicated on the accurate determination of an unknown profile at some time in the past to form the initial condition. In this study, we use both analytical solutions and a numerical model to demonstrate that boreholes with temperature‐depth profiles recorded at multiple times can be analyzed to either overcome the uncertainty associated with estimating unknown initial conditions or to form an additional check for the profile fitting. We further illustrate that the common approach of assuming a linear initial temperature‐depth profile can result in significant errors for groundwater flux estimates. Profiles obtained from a borehole in the Veluwe area, Netherlands in both 1978 and 2016 are analyzed for an illustrative example. Since many temperature‐depth profiles were collected in the late 1970s and 1980s, these previously profiled boreholes represent a significant and underexploited opportunity to obtain repeat measurements that can be used for similar analyses at other sites around the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".