Application of in situ target-strength estimations in lakes: examples from rainbow-smelt surveys in Lakes Erie and Champlain
Bibliographic record
Abstract
Abstract Acoustic abundance of fish depends directly on the target strength (TS) of the fish surveyed. We analyzed 70 and 120 kHz acoustic data from two lakes with abundant rainbow-smelt (Osmerus mordax) populations. Using repeated surveys through the summer growing season, we derived a relationship between TS (dB) and fish length L (cm) at 120 kHz (TS = 19.9 log10 L − 67.8). Values for 70 kHz were similar. In situ TS increased with fish density, indicating a bias from accepting multiple targets at high fish densities. Correcting for this bias increased estimates of smelt abundance by up to 18% in Lake Erie and up to 100% in Lake Champlain. Multiple modes in the TS distributions observed for older fish do not reflect different size groups, as the same modes can be observed from measurements from a single fish. Smelt released gas bubbles during the evening ascent, and these bubbles had TS (−60 to −58 dB) within the range of TS observed from the fish. Gas-bubble release occurred mostly during the migration. Conducting surveys after the ascent is completed will decrease bias associated with counting bubbles as fish.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".