Bed load bias: Comparison of measurements obtained using two (76 and 152 mm) Helley‐Smith samplers in a gravel bed river
Bibliographic record
Abstract
We assess how the size of the Helley‐Smith (HS) bed load sampler nozzle affects the accuracy of bed load sampling. Semitheoretical considerations show that the larger grains resident on the streambed can influence the sample either by blocking the sampler entrance or by causing the sampler to rest in a “perched” position. Probabilities for interference can be derived from the distribution of grain sizes but they do not capture the actual complexity of the influence of the bed on sampler performance. We therefore make an empirical comparison of sediment trapped by HS samplers with 76‐ and 152‐mm intakes during floods in the gravel bed lower Ebro River. Most bed load rates appeared higher when sampled with the HS152. The largest clasts collected by the HS76 also tend to be smaller than those obtained with the HS152 at the same flow. Analyzing paired bed load samples, we find the probability of a bed load sample collected with the HS152 to be biased is around 43% in the conditions of the present study, whereas 65% of samples were biased when obtained with the HS76. The analysis emphasizes the influence of bed material texture over sampler performance and demonstrates that the use of samplers with intake size much larger than bed grain size (i.e., ∼5 D ) will increase the accuracy of bed load grain size distributions and the precision of annual load estimates in gravel bed rivers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".