Length measurement accuracy of adaptive resolution imaging sonar and a predictive model to assess adult Atlantic salmon (<scp><i>Salmo salar</i></scp>) into two size categories with long‐range data in a river
Bibliographic record
Abstract
Imaging sonars are used around the world for fish population monitoring. The accuracy of the length measurements has been reported in multiple studies for relatively short (<15 m) ranges and high image resolution. However, imaging sonars are often used at longer ranges (i.e., >15 m) where the images produced from sonar returns become less detailed. The accuracy of the length measurements from the Adaptive Resolution Imaging Sonar (ARIS) was tested by releasing n = 69 known-sized adult Atlantic salmon (Salmo salar) directly into the sonar field at ranges between 15 and 29 m, and measuring their echoes manually by four users and semi-automatically using a computer workflow in Echoview software. Overall, the length measurements were very variable: compared to true (fork) lengths, the mean of differences varied between -9.9 cm and 7.8 cm in the human-generated datasets, and between -42.8 cm and -20 cm in the computer-generated dataset. In addition, the length measurements in different datasets were only in poor or moderate agreement with each other (intraclass correlation <0.61). Contrary to our expectations, the distance from the transducer or the subjectively assessed echo quality did not have an effect on the measurement accuracy in most of the datasets and when it did, the effect was not systematic between the datasets. Therefore, a size class and length prediction model was implemented in a Bayesian framework to group salmon into two size categories: One-Sea-Winter (<63 cm) and Multi-Sea-Winter (≥63 cm) groups. The model correctly predicted the size category in 83% of the fish in the computer-generated dataset and ranged from 68% to 74% in the human-generated datasets. We conclude that fish length measurements derived from long-range imaging sonar data should be used with caution, but post-processing can improve the usefulness of the data for specific purposes, such as adult Atlantic salmon population monitoring.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".