Dual‐gear approach for calibrating electric fishing capture efficiency and abundance estimates
Bibliographic record
Abstract
Abstract Electric fishing depletion systematically underestimates fish abundance in streams. Electric fishing mark‐recapture estimates are unbiased, but only when performed over several days to allow marked fish to recover from electric fishing. A mark‐recapture procedure that can be performed in 1 day and produces unbiased population estimates is described. It involves minnow trapping, marking and releasing juvenile salmonids in a stop‐netted reach, followed by electric fishing the reach 1 h after release of marked fish. Recapture of marked fish during electric fishing can form the basis of a dual‐gear mark‐recapture population estimate, or an unbiased estimate of electric fishing capture efficiency. The marking technique did not affect short‐term catchability of the target species ( P = 0.823), and provided unbiased estimates of capture efficiency (0.38–0.40) that were similar to those documented by other researchers. However, local validation to confirm equal catchability of marked and unmarked fish, as described in this study, is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".