Where and When to Collect Tracer Data to Diagnose Hillslope Permeability Architecture
Bibliographic record
Abstract
Abstract The permeability architecture has a major influence on hillslope flow path and hydrogeochemistry. To constrain this architecture and overcome equifinality in the diagnosis of hillslope flow paths within hydrologic transport models, different types of complementary data (e.g., tracer) have been recommended. However, there is still little information on the extent to which such complementary data can unravel the permeability architecture, and where and when to measure such data to most efficiently constrain models. Here, we couple a Richards‐based flow and transport model with extensive long‐term field measurements to compare the relative value of different types of hydrometric and tracer data in discriminating between contrasting permeability (or saturated hydraulic conductivity ( )) architectures, in the absence of macropore flow. Our results show that compared to streamflow and water table observations, stream tracer data have a stronger evaluative potential to constrain hillslope vertical pattern in , in particular during seasons when flow is on average low (e.g., winter or summer). Tracer data from within the hillslope are even more helpful to discriminate between different vertical patterns in Ks than stream tracer data. This suggests a higher evaluative potential for hillslope tracer observations. This evaluative potential of hillslope data depends on where and when the data are collected, and increases with depth from the soil surface, with distance from the stream and during seasons when flow is low. The findings also emphasize the importance of incorporating hillslope permeability architecture in hydrologic transport models in order to reduce the uncertainty in the predictions of stream water quality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".