Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics
Bibliographic record
Abstract
Abstract Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".