Comparison of multifrequency acoustic and <i>in situ</i> measurements of zooplankton abundances in Knight Inlet, British Columbia
Bibliographic record
Abstract
An investigation of midwater zooplankton aggregations in a coastal fjord was conducted in November 2002. This study focused on quantitative comparisons between a calibrated, three-frequency (38, 120, and 200 kHz) vessel-based echo-sounder, a multinet towed zooplankton sampler (BIONESS), and a high-resolution underwater camera (ZOOVIS). Daytime layers of euphausiids and amphipods near 70-90-m depth were observed in lower parts of the inlet, especially concentrated by tidal flows around a sill. Quantitative backscatter measurements of euphausiids and amphipods, combined with in situ size and abundance estimates, and using an assumed tilt-angle distribution, were in agreement with averaged fluid-cylinder scattering models produced by Stanton and Chu [ICES J. Mar. Sci. 57, 793-807, (2000)]. Acoustic measurements of physonect siphonophores in the upper inlet were found to have a strong 38-kHz scattering strength, in agreement with a damped bubble scattering model using a diameter of 0.4 mm. In relatively dense euphausiid layers, ZOOVIS abundance estimates were found to be a factor of 2 to 4 higher than the acoustic estimates, potentially due to deviations from assumed euphausiid orientation. Nocturnal near-surface euphausiid scattering exhibited a strong (15 dB) and rapid (seconds) sensitivity to vessel lights, interpreted as due to changing animal orientation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".