Deleterious effects of net clogging on the quantification of stream drift
Bibliographic record
Abstract
Drift studies are central to stream and river ecological research. However, a fundamental aspect of quantifying drift — how net clogging affects the accuracy of results — has been widely ignored. Utilizing approaches from plankton and suspended sediment studies in oceanography and hydrology, we examined the rate and dynamics of net clogging across a range of conditions. We found that nets clog nonlinearly over time and that suspended solid concentrations and net mesh size exerted a strong effect on clogging rates. Critically, net clogging introduced unpredictable biases in resultant data due to the inaccuracies in water volume estimates introduced by progressive clogging. This renders the widespread approach to linearly “correct” for clogging inadequate. Using a meta-analysis of 77 drift studies spanning 25 years, we demonstrate that the detrimental effects of net clogging are routinely unappreciated, even though the results of most of these studies were likely affected by clogging. We close by describing an approach for avoiding net clogging, which will increase the accuracy and reproducibility of results in future freshwater, lotic drift studies.
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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 | 0.167 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".