Use of underwater video to assess freshwater fish populations in dense submersed aquatic vegetation
Bibliographic record
Abstract
Underwater video cameras (UVC) provide a non-lethal technique to sample fish in dense submersed aquatic vegetation. Fish often inhabit densely vegetated areas, but deficiencies of most sampling gears bias relative abundance estimates that inform fisheries management. This study developed methods using UVC to estimate relative abundance in dense vegetation using three experimental ponds covered with surface-matted hydrilla (Hydrilla verticillata) stocked at different densities of Lepomis spp. and largemouth bass (Micropterus salmoides). We conducted UVC point counts over 13 weeks to measure relative fish abundance and occurrence from video analysis. Ponds were then drained to obtain true fish densities. In total, fish were detected in 55% of all counts and juvenile and adult Lepomis spp. and largemouth bass were enumerated. End-of-season true fish densities ranged across ponds (from 52 to 37 000 fish ha–1). Additionally, pond 2’s true density changed substantially from 370 to 12 300 fish ha–1. True population size was accurately reflected in differences in estimated relative abundances obtained from fish counts, as in pond 2 where mean fish counts increased from 0.10 in week 1 to 2.33 by week 13. Underwater video accurately and precisely quantified relative abundance at naturally-occurring fish densities, but this success was reduced at low densities.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".