Retrospective Sampling Strategies Using Video Recordings to Estimate Fish Passage at Fishways
Bibliographic record
Abstract
Abstract Reliable estimates of population size are critical for fisheries management and for testing ecological hypotheses but can be expensive and time consuming to obtain. Sampling methodologies have been developed to obviate complete enumeration, but their effectiveness can be limited by logistical constraints. A posteriori sampling from digital video recordings, however, permits the application of otherwise impractical sampling schemes. We evaluated the time savings of estimating total run size by a posteriori sampling of video recordings (assisted by motion detection software) in comparison with on-site counting methods. The evaluation was based on 9 years of complete counts enumerated either on site or by a posteriori counts from video recordings of alewives Alosa pseudoharengus migrating through a fishway in Nova Scotia. We compared results obtained using analytical estimation methods with results from simulation-based methods and found them to be the same for simple random sampling and daily stratified random sampling. We tested the application of using motion detection software to automatically omit sample units with zero counts from sampling strategies; large reductions in sampling requirements were obtained for data sets with large proportions of zero counts. We also evaluated the effect of sample unit size on sampling effort requirements; use of short but frequent sample units allowed for large reductions in sampling effort. Use of motion detection software in combination with shorter sample units achieved highly significant aggregate reductions in sampling effort. For example, at the shortest tested sample unit size of 1 min, it was possible to reduce sampling requirements to 4 min/d based on daily stratified random sampling to achieve an estimate of the true population within 20% and with 95% confidence. Finally, we evaluated linear interpolation to estimate fish passage for missed days; although average bias was small, bias was substantial when a peak run day was missed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".