Optimizing the use of portable ROVs for community-based benthic surveys: A case study on the demographics of sea cucumbers around nursery habitats in the Arctic
Bibliographic record
Abstract
Nearshore areas around Qikiqtait in the Canadian Arctic have been identified as nursery habitats for the sea cucumber Cucumaria frondosa , which is locally consumed and being explored for small-scale commercial fisheries. This study characterized this species’ demographics along a depth gradient (∼0.5–11.5 m) at three sites inside and adjacent to nursery grounds using a mini-class remotely operated vehicle (ROV). A novel technique involving the superimposition of virtual quadrats was developed to minimize perspective biases for the analysis of images captured obliquely relative to the seafloor. Overall densities at Katak and Kataaluk were a magnitude lower than at Sanikiluaq, suggesting spatial variability in environmental conditions. Smaller individuals (∼5 cm) occurred mostly on rubble between 1–3 m at Sanikiluaq, larger ones (∼10 cm) on gravel at 2–7 m at Kataaluk, and a mix of both size classes occupied gravel, rubble, boulders, and bedrock between 3–11 m at Katak. Globally, body sizes tended to increase with depth, evoking cohorts of sea cucumbers undergoing step-wise downward migrations as they grew. This study provides foundational data on C. frondosa around its nursery habitats. It also enhances the usefulness of mini-class ROVs to investigate benthic assemblages in ice-covered areas through community-led initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".