Comparing the Performance of a Remotely Operated Vehicle, a Drop Camera, and a Trawl in Capturing Deep-Sea Epifaunal Abundance and Diversity
Bibliographic record
Abstract
Deep-sea ecosystems provide services such as food, minerals, and nutrient recycling, yet baseline data on their structure is often lacking. Our limited knowledge of vulnerable deep-sea ecosystems presents a challenge for effective monitoring and mitigation of increasing anthropogenic threats, including destructive fishing and climate change. Using data from two stations differing in total epifaunal abundance and taxonomic composition, we compared the use of imagery collected by two non-invasive tools [remotely operated vehicle (ROV) and drop camera] and data collected with a trawl system, commonly used to quantify epibenthic megafauna in the deep sea. Imagery and trawl data captured different epifaunal patterns, the former being more efficient for capturing epifauna, particularly Pennatulacean recruits. The image-based methods also caused less disturbance, had higher position accuracy, and allow for analyses of spatial structure and species associations; fine-scale distributions could not be elucidated with a trawl. Abundance was greater for some taxa, and diversity accumulated faster with increasing sample size for the drop camera than the ROV at one station. However, there are trade-offs between these tools, including continuous and discrete sampling for the ROV and drop camera, respectively, which can affect follow-up analyses. Our results can be used to inform monitoring frameworks on the use of appropriate sampling tools. We recommend further research into tool sampling biases and biometric relationships to help integrate datasets collected with different tools.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".