Quantitative guidance for efficient vertical flow measurements at the sediment–water interface using temperature–depth profiles
Bibliographic record
Abstract
Abstract Upward discharge to surface water bodies can be quantified using analytical models based on temperature–depth (T‐z) profiles. The use of sediment T‐z profiles is attractive as discharge estimates can be obtained using point‐in‐time data that are collected inexpensively and rapidly. Previous studies have identified that T‐z methods can only be applied at times of the year when there is significant difference between the streambed–water interface and deeper sediment temperatures (e.g., winter and summer). However, surface water temperatures also vary diurnally, and the influence of these variations on discharge estimates from T‐z methods is poorly understood. For this study, synthetic T‐z profiles were generated numerically using measured streambed interface temperature data to assess the influence of diurnal temperature variations on discharge estimation and provide insight into the suitable application of T‐z methods. Results show that the time of day of data collection can have a substantial influence on vertical flux estimates using T‐z methods. For low groundwater discharge fluxes (e.g., 0.1 m d−1), daily transience in streambed temperatures led to relatively large errors in estimated flow magnitude and direction. For higher discharge fluxes (1.5 m d−1), the influence of transient streambed temperatures on discharge estimates was strongly reduced. Discharge estimates from point‐in‐time T‐z profiles were most accurate when the uppermost point in the T‐z profile was near the bed interface daily mean (two time periods daily). Where temperature time series data are available, daily averaged T‐z profiles can produce accurate discharge estimates across a wide range of discharge rates. Seasonality in shallow groundwater temperature generally had a negligible influence on vertical flow estimates. These findings can be used to plan field campaigns and provide guidance on the optimal application of T‐z methods to quantify vertical groundwater discharge to surface water bodies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".