Biases of submerged bulk and freeze‐core samples
Bibliographic record
Abstract
Abstract Freeze‐coring and bulk sampling are routine methods used to sample subsurface spawning gravel under shallow water. Both methods have limitations. Freeze‐coring is not believed to representatively sample coarse grain sizes and the sample volumes are relatively small. Conversely, when bulk sampling, even within an enclosure, some fine sediment is suspended and washed away from the sample. This paper assesses the biases in sampling performance between the two methods and determines whether the loss of fines that occurs when bulk sampling could be predicted and thus corrected for. At six riffles the spawning substrate was sampled under approximately 50 cm of water with a bulk sample and three adjacent freeze‐cores. For each riffle, data from the two samples were combined using the method of Fripp and Diplas (1993) and the resultant composite sample was compared with the original freeze‐core and bulk samples to assess the relative precision and biases of the two techniques. On average, the D50 of the bulk samples was 4 mm larger and a one‐third loss of the <2 mm fraction occurred compared with the composite samples. In contrast, freeze‐core samples contain on average 32% more sediment >16 mm compared with composite samples. Based on six samples, taken from six riffles, the amount of sediment finer than 0·5 mm lost using our bulk sampling technique with an enclosure appears to be predictable and correctable. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".