Challenges in measuring fine sediment ingress in gravel‐bed rivers using retrievable sediment trap samplers
Bibliographic record
Abstract
Abstract “Excess” interstitial fine sediment (<2 mm) is known to cause deleterious impacts on streambed ecosystems. Current methodologies available to assess ingress and its vertical and horizontal components still lack standardization, and the accuracy of commonly used assessments is still debatable. Here, we evaluate three fine sediment trap designs that measure only vertical (V), only horizontal (H), and both vertical and horizontal (HV) ingress mechanisms. Sediment traps were deployed in triplicates to: (i) evaluate measurement variability within traps of the same type; (ii) evaluate the effects of trap design on particle size distributions of infiltrated fine sediment and; (iii) assess methodologies used to calculate vertical and horizontal ingress mechanisms. Ingress rates were recorded for each sediment trap during seven deployment periods (lasting from 2 to 10 days) at a range of flow conditions at four sites. A total of 252 traps were deployed. Results from the triplicate assessment of traps with the same design showed that most measurements presented high variability and that particle size distributions were significantly affected by trap design. Here, different sediment traps were able to estimate directional ingress mechanisms. However, direct comparison between HV with either H or V traps led to an overestimation of horizontal or vertical ingress mechanisms, respectively. Better estimations were found when comparing HV observations to half the accumulation in either H or V, due to the proportional trap volume available for each accumulation mechanism according to trap design.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".