Sampling stream invertebrates using electroshocking techniques: implications for basic and applied research
Bibliographic record
Abstract
We present a new technique using electrofishing equipment to collect and quantitatively sample stream invertebrates. We used an electrofishing machine with a small anode to produce a localized field of pulsed direct current to induce invertebrate drift. We quickly obtained large numbers of live invertebrates for experiments by passing the anode over the stream bottom upstream of sampling nets. We compared the results of five techniques: (i) electroshocking inside a modified Hess sampler, (ii) repeated electroshocking over a large area to estimate population size by depletion, (iii) traditional Surber, (iv) Hess, and (v) individual stone sampling. Electroshocking techniques provided estimates of invertebrate density comparable with those of traditional sampling techniques. The electroshocking depletion method that sampled a large area provided higher measures of Ephemeroptera, Plecoptera, and Trichoptera richness. Hess and area-restricted electrobug methods had similar density and diversity estimates, whereas the Surber sampler provided low density estimates, especially for mobile taxa. Density estimates from individual stones were inflated, were biased for mayflies, and had low richness. Samples taken with the electroshocking method were processed 40% faster because these samples contained little detritus. Electroshocking techniques can provide accurate estimates of population size and diversity, minimize disturbance to benthic habitats, and reduce processing time.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".