Effects of solute breakthrough curve tail truncation on residence time estimates: A synthesis of solute tracer injection studies
Bibliographic record
Abstract
Hydrologic transport and retention strongly affect biogeochemical processes that are critical to stream ecosystems. Tracer injection studies are often used to characterize solute transport and retention in stream reaches, but the range of processes accurately resolved with this approach is not clear. Solute residence time distributions depend on both in‐stream mixing and exchange with the hyporheic zone and the larger groundwater system. Observed in‐stream breakthrough curves have most commonly been modeled with in‐stream advection‐dispersion plus an exponential residence time distribution, but process‐based models suggest that hyporheic exchange is a fractal process, and that hyporheic residence time distributions are more appropriately characterized by power law tailing. We synthesized results from a variety of tracer‐injection studies to investigate the information content of tracer breakthrough curves. We found that breakthrough curve tails are often not well characterized in stream tracer experiments. The two main reasons for this are: 1) experimental truncation of breakthrough curves, which occurs when sampling ends before all tracer mass reaches the sampling location, and 2) sensitivity truncation of breakthrough curves, when tracer concentrations in the tail are too low to be detected reliably above background levels. Tail truncation reduces observed mass recovery and obscures assessment of breakthrough curve tailing and solute residence time. Failure to consider tail truncation leads to underestimation of hyporheic exchange and solute retention and to corresponding overestimation of hyporheic biogeochemical transformation rates. Based on these findings, we propose criteria for improved design of in‐stream tracer injection experiments to improve assessment of solute tailing behavior.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".