Point Sampling by Boat Electrofishing: A Test of the Effort Required to Assess Fish Communities
Bibliographic record
Abstract
Abstract Point sampling by electrofishing is often used to study fishes in large rivers and lakes whereby a specific location is electrofished without moving the anode. Short (1–5-s) samples are taken under the belief that many small samples are preferred over a few large ones for statistical analyses. However, this typically results in relatively little time spent sampling fishes compared with time spent measuring abiotic factors and traveling among sites. We evaluated the optimal sampling duration and number of replicates per site to balance sample size and number for community-level studies. In 2004, 165 point samples were taken from shallow Canadian waters of the Detroit River. Sites were continuously electrofished for 2 min (eight 15-s intervals), and a second replicate of 2 min was taken after a pause. Subsets of the data were used to compare various designs of sampling duration and number of replicates. A sampling design of two replicates of 1 min appeared to be ideal because it balanced a large gain of information with a small increase in effort. This design would allow 35–50 sites to be sampled per day, depending on the detail of abiotic measurements. Compared with data from the first 15-s interval only, sampling for two replicates of 1 min resulted in fewer null (no fishes captured) samples (19% instead of 53%). The number of common (found at >5% of samples) species also increased from 12 to 19. By increasing the effort for point sampling by electrofishing at each site, a better understanding of the fish assemblage was obtained. This allows for more complete analyses of community composition and habitat preference.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".