Terrestrial diatoms as tracers in catchment hydrology: a review
Why this work is in the frame
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Bibliographic record
Abstract
Diatoms are remarkable organisms. They are present in almost all habitats containing water (e.g., lakes, streams, soils, bark) and rank among the most common algal groups in both freshwaters and marine ecosystems. The ubiquitous character of aquatic diatoms has triggered countless applications as environmental tracers for studies in water quality, paleoclimate reconstruction and sediment tracing. However, diatoms also occur in the terrestrial environment. It is this plethora of diatom life‐forms that has recently triggered interest in their use as tracers of hydrological processes. The use of diatoms in catchment hydrology has been very limited. Part of the reason is that until recently, the taxonomy and ecology of terrestrial diatom assemblages were largely unknown. However, in the past decade, much work has been done to quantify terrestrial diatom reservoir size, dynamics, and potential depletion following precipitation events. Therefore, such terrestrial diatoms now hold promise for use in catchment hydrology—for tracing runoff flow sources and pathways across a wide range of spatial scales. Here we review the literature on terrestrial diatoms and describe the various sampling protocols that have been designed and tested for specific applications in hydrological processes research. We review and summarize the work on terrestrial diatom reservoir characterization, transport mechanisms and pathways to show how such diatom‐based tracer work might be possible at the catchment scale for rainfall‐runoff studies. Finally, we present a vision for future work that might take advantage of terrestrial diatoms in catchment hydrology and discuss the main challenges going forward. WIREs Water 2017, 4:e1241. doi: 10.1002/wat2.1241 This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.027 |
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 it