A new approach to the application of electrical resistance sensors to measuring the onset of ephemeral streamflow in wetland environments
Bibliographic record
Abstract
Ephemeral streamflow events in headwater catchments are significant in terms of the flux of sediments, solutes, and discharge out of a catchment. Existing attempts to monitor these events, however, have traditionally been restricted to a limited series of manual observations or the use of temperature sensors which demand a great deal of data interpretation and often introduce significant timing errors. The use of electrical resistance sensors has been found to be one potential alternative, but this method has not yet been fully explored. This paper builds upon this method, presenting a new low‐cost ephemeral streamflow (ES) sensor which is able to detect the onset and cessation of ephemeral streamflow events at high spatial and temporal resolutions. Furthermore, the data collected by the ES sensor needs only minimal interpretation. Laboratory testing reveals that the sensors are able to clearly distinguish between the presence and absence of water. Field testing in a small peatland headwater catchment in the South Pennines, United Kingdom, confirmed that the sensors were robust enough to withstand field conditions. Careful site selection enabled the production of a high‐quality data set, showing the timings of multiple ephemeral streamflow events at numerous locations within the catchment. The low cost, good performance, and minimal data interpretation requirements of the ES sensors permit unprecedented high‐resolution monitoring of ephemeral streamflows.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".