Using high‐resolution distributed temperature sensing to quantify spatial and temporal variability in vertical hyporheic flux
Bibliographic record
Abstract
Hyporheic flow can be extremely variable in space and time, and our understanding of complicated flow systems, such as exchange around small dams, has generally been limited to reach‐averaged parameters or discrete point measurements. Emerging techniques are starting to fill the void between these disparate scales, increasing the utility of hyporheic research. When ambient diurnal temperature patterns are collected at high spatial resolution across vertical profiles in the streambed, the data can be applied to one‐dimensional conduction‐advection‐dispersion models to quantitatively describe the vertical component of hyporheic flux at the same high spatial resolution. We have built on recent work by constructing custom fiber‐optic distributed temperature sensors with 0.014 m spatial resolution that are robust enough to be installed by hand into the streambed, maintain high signal strength, and permit several sensors to be run in series off a single distributed temperature sensing unit. Data were collected continuously for 1 month above two beaver dams in a Wyoming stream to determine the spatial and temporal nature of vertical flux induced by the dams. Flux was organized by streambed morphology with strong, variable gradients with depth indicating a transition to horizontal flow across a spectrum of hyporheic flow paths. Several profiles showed contrasting temporal trends as discharge decreased by 45%. The high‐resolution thermal sensors, combined with powerful analytical techniques, allowed a distributed quantitative description of the morphology‐driven hyporheic system not previously possible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".