Hydroacoustic measures of <i>Mysis relicta</i> abundance and distribution in Lake Ontario
Bibliographic record
Abstract
Mysis relicta can be observed on echograms as a sound scattering layer when they migrate into the water column at night to feed on zooplankton. However, quantitative measures of mysid abundance with hydroacoustics requires knowledge of mysid target strength (TS), a method of removing fish echoes and contribution from noise, and an understanding of the effect of range on the ability of hydroacoustics to detect mysids (the detection limit). Comparisons of paired net data and acoustics data from July 7, 2005 yielded a mysid TS of −86.3 dB (9 mm animal) and a biomass TS of −58.4 dB (g dry wt)−1. With ambient noise levels (Sv of −125 dB at 1 m depth) and this TS, we can detect a mysid density of 1 m−3 at 60 m depth with a signal to noise ratio of 3 dB. We present a method to remove backscattering from both noise and fish and apply this method and the new TS data to whole lake acoustic data from Lake Ontario collected in July 25–31, 2005 with a 120 kHz echosounder as part of the annual standard fish survey in that lake. Mysis abundance was strongly depth dependent, with highest densities in areas with bottom depth > 100 m, and few mysids in areas with bottom depth < 50 m. With the data stratified in five bottom depth strata (> 100 m, 100-75 m, 75–50 m, 50–30 m, < 30 m), the whole-lake average mysid density was 118 m−2 (CV 21%) and the whole-lake average mysid biomass was 0.19 g dry wt m−2 (CV 22%) in July 2005. The CVs of these densities also account for uncertainty in the TS estimates. This is comparable to whole-lake density estimates using vertical net tows in November, 2005 (93 m−2, CV 16%).
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".