Accounting for biological and physical sources of acoustic backscatter improves estimates of zooplankton biomass
Bibliographic record
Abstract
To convert measurements of backscattered acoustic energy to estimates of abundance and taxonomic information about the zooplankton community, all of the scattering processes in the water column need to be identified and their scattering contributions quantified. Zooplankton populations in the eastern edge of Wilkinson Basin in the Gulf of Maine in the Northwest Atlantic were surveyed in October 1997. Net tow samples at different depths, temperature and salinity profiles, and multiple frequency acoustic backscatter measurements from the upper 200 m of the water column were collected. Zooplankton samples were identified, enumerated, and measured. Temperature and salinity profiles were used to estimate the amount of turbulent microstructure in the water column. These data sets were used with theoretical acoustic scattering models to calculate the contributions of both biological and physical scatterers to the overall measured scattering level. The output of these predictions shows that the dominant source of acoustic backscatter varies with depth and acoustic frequency in this region. By quantifying the contributions from multiple scattering sources, acoustic backscatter becomes a better measure of net-collected zooplankton biomass.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".