The use of moored inverted echo sounders for monitoring meso-zooplankton and fish near the ocean surface
Bibliographic record
Abstract
This study evaluates the effectiveness of a 200-kHz inverted echo sounder for monitoring the abundance and behavior of near-surface zooplankton and fish. Data from both oceanic and littoral environments are examined: first from an 81-day deployment at Ocean Station Papa (OSP) in the northeast Pacific Ocean during the spring of 1996, and second from an 8-day deployment in the southern Strait of Georgia in September 1998. The analysis combines calibrated backscatter intensity, echo-amplitude statistics, and acoustic-scattering models to produce estimates of mean scatterer size and abundance. The identity of the various scatterer classes is deduced from local net trawls and reference to previous studies. At the OSP site the dominant scatterers were found to be euphausiids, pteropods, and myctophid fishes, with mean lengths of 15, 1.5, and 28 mm, respectively. At the Strait of Georgia site three fish size classes were identified: juvenile herring with mean length near 10 cm, juvenile salmon with mean length of 20 cm, and there was weak evidence for an adult salmon class. Overall, the acoustically derived abundance estimates were in reasonable agreement with the local net trawls and results from previous studies. The usefulness of sustained monitoring over diurnal and seasonal time scales is demonstrated with the OSP zooplankton data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".