Identifying fish and estimating abundance and swim velocities of migrating Pacific salmon using adaptive resolution imaging sonar in mobile surveys
Bibliographic record
Abstract
Abstract Mobile acoustic sounding is an effective survey method for fish abundance residing or migrating in large riverine basins and marine areas. A long-standing challenge in acoustic fish surveys with conventional sonar is the uncertainty in identifying fish targets from acquired echo data. Identification errors of fish targets can significantly bias estimates of fish abundances and negatively impact the management of fisheries. In contrast to conventional sonar, adaptive resolution imaging sonar (ARIS), if deployed properly with appropriate settings, can yield high-quality images of fish targets. ARIS images acquired with adequate frame rates can form video recordings to allow for confident identification of fish targets from recorded morphological features, sizes, direction of movements, and speeds. In this paper, we present an approach of using ARIS sonar for mobile surveys of fish passage in a riverine environment. Applications of this approach are demonstrated for its practical values with results from an ARIS-based mobile survey of upstream migrating salmon at an acoustically challenging fish-counting site on the lower Fraser River in British Columbia, Canada.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".