Sediment trapping characteristics of a pit trap and the Helley‐Smith sampler in a cobble gravel bed river
Bibliographic record
Abstract
Although designed for granule gravels, the Helley‐Smith (HS) bed load sampler is often used in streams with much coarser beds. To compare magnitudes and grain size distributions of sediment samples collected by a pit trap and a standard HS sampler, we conducted 22 “sampling events” over a wide range of flows during a snowmelt freshet in a stream with a coarse gravel bed. Each sampling event consisted of three simultaneous measurements: one made with a pit trap installed in the bed, one with a HS sampler placed on the bed directly beside the pit trap (HS‐I), and one with a HS sampler placed on the downstream rim of the pit trap (HS‐P). We summed the catches of the pit trap and HS‐P to estimate “actual” bed load transport. The pit trap and HS‐I catches were then compared to the summed sample. The pit trap yielded a remarkably consistent, positively skewed, sigmoidal distribution of catch efficiency for all 22 measurements, with near 100% efficiency for material larger than 2.8 mm. The HS‐I sampler was more variable in its catch and trapping efficiency, exhibiting low trapping efficiency for midrange material (0.71 to 16 mm) but high efficiency for finer material. The results cast doubt on the accuracy of bed load data sets collected by Helley‐Smith samplers in coarse gravel channels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".