Comparison of Paper- and Electronic-Formatted Hydroacoustic Data Charts used for Salmon Enumeration on the Yukon River near Pilot Station
Bibliographic record
Abstract
The Yukon River Sonar Project estimates salmon passage through the river near Pilot Station, Alaska. The hydroacoustic data collected by the sonar is currently printed on paper charts in a series of grey marks called “traces.” Technicians count traces that were generated by fish, and these numbers are used to calculate daily abundance estimates. New technology allows the hydroacoustic data to be presented on electronic charts viewed on a computer. The electronic charts also present the data in a series of grey marks, and fish traces must be identified manually by technicians. However, the electronic charts present the data in greater detail, and settings that are used to optimize the visibility of fish traces are more easily adjusted. Both of these features may improve fish detection, which would result in more accurate estimates. Project leaders are planning to make a complete switchover from paper to electronic charts. The principle aim of this study was to compare the fish counts produced by the paper and electronic formats in order to expose any biases and explain why they occur. Due to variation in the slope of the river bottom, the area of river covered by the sonar is divided into several horizontal strata by distance from the transducer. Due to the properties of sound and the variation in the shape of fish traces at different ranges, it is possible that the level and direction of bias may differ among strata. A sample of 150 electronic files, out of approximately 1,700, from the 2008 season was selected for this comparison. Files were counted using Echotastic, a program written by AYK Regional Sonar Biologist, Carl Pfisterer. The electronic chart counts were higher than the paper chart counts for strata one through four, while the electronic counts were lower than the paper counts for stratum five (linear regression output: stratum one: slope=1.112, y-intercept=44.662, stratum two: slope=1.344, y-intercept=13.615, stratum three: slope=1.098, y-intercept=-7.052, stratum four: slope=1.077, y-intercept=-8.566, stratum five: slope=0.827, y-intercept=-0.688). Both the positive and negative biases are likely a result of improved fish detection on the electronic charts and a high level of subjectivity associated with counting fish using sonar. If project leaders conclude that these biases are acceptable, a transition from paper to electronic charts would be advantageous, although correcting for differences will be necessary to make past and future fish estimates comparable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".