The effectiveness of two common sampling methods for assessing imperilled freshwater fishes
Bibliographic record
Abstract
This study tested the hypothesis that the most common gear type used to sample fishes in wadeable systems, electrofishing, was more effective than another commonly used gear type, seining, for sampling fish species at risk. Five predictions were tested. At sites where species at risk were detected, (1) the probability of detecting the species at risk, (2) the probability of only one gear type detecting the species at risk and (3) the estimated catch per unit effort of the species at risk, was as high as, or higher, when using electrofishing than when using a seine. (4) The number of sample sites required to detect a species at risk within a watershed and (5) the number of subsections required to detect a species at risk within a site, were as low as, or lower, using electrofishing than the number required using a seine. Based on analyses of these measurements, electrofishing was a more effective gear type than seining for sampling fish species at risk, irrespective of the unit (presence or absence or catch per unit effort) or scale of measurement (watershed or site level). Dissolved oxygen, turbidity, specific conductivity and nitrate concentrations were measured at each site and did not account for the between gear differences. Selection of sampling gear can be a fundamental consideration for the assessment of fish species at risk, where, unlike common species, they may be particularly influenced by small population sizes, restricted geographic ranges and narrow habitat preferences. Resource managers must weigh differences in the risks of injury of fish species at risk against differences in the effectiveness of each gear type when deciding between gear types and the utility of the assessments they represent.
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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 | 0.035 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".