Temporary streams in a peatland catchment: pattern, timing, and controls on stream network expansion and contraction
Bibliographic record
Abstract
ABSTRACT In peatlands, poorly maintained baseflows mean that network expansion during storm events can be rapid and pronounced, resulting in large changes in catchment connectivity. This has implications for the timing and magnitude of material fluxes from these environments, understanding of which is becoming increasingly important due to peatlands' significance as global carbon stores. In this study, electrical resistance (ER) technology has been used to create sensors capable of detecting the presence and absence of flow in ephemeral portions of the channel network. These sensors provide data on the patterns of network variation in the Upper North Grain research catchment, a small peatland headwater in the South Pennines, UK. Networks of around 40 sensors were deployed in autumn 2007 and summer 2008, giving a total of almost four months of high‐resolution monitoring data. Drainage density in the catchment was found to vary between 1.4 and 30.0 km/km2, suggesting significant differences in connectivity between the expanded and contracted networks. Water table depth was identified as the key factor determining the temporal pattern of streamflow at both the site‐ and catchment‐wide scales. Spatially, network expansion and contraction occurred in a disjointed manner, following a similar pattern between events, suggesting that localized controls are important for flow generation. Spatial controls on flow generation relate to local water table levels, and include drainage area, local dissection, channel slope and gully morphology. The importance of water table as the key control on catchment connectivity suggests that potential future change in catchment water tables, associated with projected climate change or with peatland restoration by rewetting, will modify the frequency of full catchment connectivity. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.000 | 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".