Inverse method applied to autonomous broadband hydroacoustic survey detects higher densities of zooplankton in near-surface aggregations than vessel-based net survey
Bibliographic record
Abstract
Throughout all oceans, aggregations of zooplankton and ichthyoplankton appear as horizontal sound scattering layers (SSLs) when detected with active acoustic techniques. Quantifying the composition and density of these layers is prone to sampling biases. We conducted a net and trawl survey of the epipelagic fauna in northern Norway (70° N) in June 2018 while an autonomous surface vehicle equipped with a broadband echosounder (283–383 kHz) surveyed the same region. Densities from the autonomous hydroacoustic survey were calculated using forward estimates from the relative density from the net and trawl, and inversion estimates with statistical data-fitting. All four methods (net, trawl, acoustic forward and inverse methods) identified that copepods dominated the epipelagic SSL, while pteropods, amphipods and fish larvae were present in low densities. The density estimates calculated with the inverse method were higher for mobile zooplankton, such as euphausiid larvae, than with the other methods. We concluded that the inverse method applied to broadband autonomous acoustic surveys can improve density estimates of epipelagic organisms by diminishing avoidance biases and increasing the spatio-temporal resolution of ship-based surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".