Using rapid and repeatable side scan sonar methods for a second assessment of the Shortnose Sturgeon (Acipenser brevirostrum) population in the Saint John River, New Brunswick, Canada
Bibliographic record
Abstract
Population estimates are a key component of fisheries management, particularly when assessing species of concern. However, the time and effort required to conduct those estimates logistically limits their frequency. To facilitate assessment of Shortnose Sturgeon (Acipenser brevirostrum; SNS) which are a species of concern in the Saint John River, New Brunswick, Canada, a combined side-scan sonar and acoustic telemetry-based method was employed to enumerate SNS within high density winter aggregations. During this study 12,005 SNS were enumerated in one main winter aggregation and 2,186 SNS were counted within a second in the Kennebecasis Bay. Winter residency patterns determined from acoustic tracking of 18 tagged SNS over 8 years (2015/16-2022/23) indicated that these two aggregations represented on average 74.3% of the overall population suggesting that the total Saint John River population was ~19,100 SNS > 40 cm FL in winter 2022/23. Although the development of more in depth, robust, and repeated assessments are needed to verify this estimate of abundance and size classes, we conclude that the abundance of SNS in the Saint John River has probably remained stable since the earliest population estimate completed in 1977.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".