Surface-sediment bioturbation quantified with cameras on the NEPTUNE Canada cabled observatory
Bibliographic record
Abstract
The mixing of deep-sea sediments by benthic megafauna is an important ecological service that influences biogeochemical processes.Quantifying the contribution of individual species to bioturbation and their responses to environmental variations requires experimental manipulation or direct observation, both of which are logistically challenging in the deep sea.Emerging cabled seafloor observatories now permit real-time data transfer to shore and interactive sampling, providing a new tool for long-term studies of the benthos at high temporal resolutions.We report on the development of a methodological approach to study surficial bioturbation by megafauna in a submarine canyon by using video cameras remotely operated over the internet, through the NEPTUNE Canada observatory.Observation protocols and image analysis techniques were developed to quantify organism size, locomotion and appearance rates for 2 flatfishes (Dover sole Microstomus pacificus and Pacific halibut Hippoglossus stenolepis) and the fragile pink sea urchin Allocentrotus fragilis.Application of a Bayesian model to extrapolate megafaunal locomotion patterns and appearance rates yielded sediment-surface reworking rates on the order of 26.0 to 35.1 cm 2 yr -1 .Future observations can be directly incorporated into the model to improve accuracy.We propose that this combined observation and Bayesian modeling approach could become a useful component of a long-term program for monitoring ecological processes on the seafloor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".