A Novel Technological Approach to Recover Aquatic Research Equipment from Depth
Bibliographic record
Abstract
Abstract Researchers are increasingly using biologging equipment (e.g., telemetry receivers, temperature loggers) to characterize the ecology of aquatic ecosystems. This equipment is commonly deployed at a wide range of water depths and greatly expands our capacity to remotely monitor aquatic ecosystems; however, equipment retrieval can be a major challenge. Here, we describe a technological solution to this challenge that uses a combination of live imaging sonar and a remotely operated vehicle to efficiently locate and recover equipment across a wide range of conditions (e.g., turbid water, range of water depths). We provide details on our specific equipment setup (total cost < Can$15,000) used for the recovery of acoustic fish tracking receivers moored to the benthos of Stoney Lake, Ontario, Canada. There are some limitations to this approach, which are discussed. With technological advances and increases in affordability of commercially available products, this approach may be widely applicable to recover biologgers from depth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".