A New Tool for Measuring Sediment Accumulation with Minimal Loss of Fines
Bibliographic record
Abstract
Abstract To measure the impact of culvert construction on brook trout Salvelinus fontinalis spawning beds, we collaborated with Bio-Innove, Inc., to develop a simple method for measuring the physical characteristics of streambeds and for quantifying the accumulation of fine sediment at spawning depth. We developed a modified version of the Wesche method that greatly limits fine-sediment loss at retrieval and the loss of the sediment collectors themselves. We found all 128 collectors after a 1–3-month period and all but 3 after 1 year in four experimental streams. The collectors are reusable and permit easy transfer of samples to the laboratory without any visible loss of fine sediment. The data collected allowed us to compare, by use of parametric statistics, the upstream and downstream sections from newly built culverts in terms of percent fines of different diameters, distance of sediment accumulation downstream, grading curves, and organic matter content of accumulated fine sediment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".