Combining Imaging Sonar Counting and Underwater Camera Species Apportioning to Estimate the Number of Atlantic Salmon and Striped Bass in the Miramichi River, New Brunswick, Canada
Bibliographic record
Abstract
Abstract A combined method incorporating an imaging sonar and underwater cameras was tested for assessing the size of adult Atlantic Salmon Salmo salar and Striped Bass Morone saxatilis populations in one of the main tributaries of the Miramichi River, New Brunswick, Canada. The number of fish recorded with the sonar in October 2019 was apportioned using the species ratio from the underwater camera data. The combined method estimated 358 Atlantic Salmon and 255 Striped Bass when the species ratio was applied every day and 274 Atlantic Salmon and 337 Striped Bass when the monthly species ratio was applied. The counts were compared to catches in a downstream index trap net using estimated values for trap-net catchability and for the proportion of fish ascending to the same tributary. Depending on the estimated values, the sonar–camera counts were between 40% and 190% of the estimated Atlantic Salmon numbers in the index net. For Striped Bass, the same estimated catchability and proportion values produced a lower agreement (sonar–camera count = 5–24% of the adjusted catch) because unlike Atlantic Salmon, Striped Bass do not deterministically migrate up the tributary in autumn. The fish were mostly detected overnight, and the trends in daily numbers of fish detected with the combined sonar–camera method were similar to the catches in the index net, with most Atlantic Salmon being detected mid-month and most Striped Bass being detected at the end of the month. The similarity of the fish counts with the adjusted trap-net catch and the new information about migration timing demonstrate that the sonar–underwater camera combination can provide tributary-specific and timely information on the Atlantic Salmon population in the Miramichi River.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".