Equipping an underwater glider with a new echosounder to explore ocean ecosystems
Bibliographic record
Abstract
Abstract Mobile autonomous platforms are revolutionizing our understanding of ocean systems by providing a solution for the four‐dimensional observation problem faced in the ocean. The sensors commonly used in autonomous platforms, however, leave a large gap in our observations of the food chain between primary producers and large predators. Echosounders have the potential to fill this gap. Here, we present details of a new, commercially available quantitative scientific echosounder specifically designed to meet the challenges of deployment in autonomous platforms, including those of relatively low power and small size, while providing data comparable to systems deployed from ships. We detail the integration into a Slocum glider of this echosounder and both upward‐ and downward‐looking transducers to provide guidance for those considering similar efforts. We also identify key features of the system and the challenges that must be overcome to ensure collection of high‐quality data. The most important feature of the integrated glider is that it carries instruments capable of providing depth profiles of bio‐optical and environmental variables that are synoptic with the echosounder data. On a dive‐by‐dive basis, we can use these co‐located data to quantify relationships between the acoustic, bio‐optical, and environmental data. A field deployment of the echosounder‐equipped glider elucidated the processes driving diel migration in zooplankton and nekton in Monterey Bay, emphasizing the novel science questions that can be addressed using contemporary means of accessing the sea and new, integrated tools for describing the habitat and its inhabitants.
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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 | 0.000 |
| 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 | 0.001 |
| 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".