Development of a Non-Cloggable Subsea Data Logger for Harsh Turbidity Current Monitoring
Bibliographic record
Abstract
Large submarine flows of sediment (sand and mud), known as turbidity currents, transfer and bury significant amounts of organic carbon and pollutants to the deep sea via submarine canyons. They are also significant geohazards, regularly breaking networks of seabed telecommunications cables that carry > 99% of global data that underpin the internet. Despite this, key parameters (notably their sediment concentration) in these flows are yet to be directly measured in real-time due to their inherently harsh environment that is unsuitable for commercial conductivity sensors. To address this issue, a subsea datalogger (SSDL) is developed with a planar conductivity sensor head that can measure the sediment concentration within dense turbidity currents. Unlike conventional sensors, the planar design of the SSDL’s sensor prevents clogging at high sediment concentrations, allowing for continuous measurements within turbidity currents. The conductivity sensor is developed with a temperature sensor which is measured using an external 16-Bit ADC which is controlled with a SAMD21 32-Bit ARM microcontroller. The SSDL measures the temperature and the conductivity of the seawater once every 4 seconds for over a year. In an initial device test, the SSDL can record a turbidity current within the Bute Inlet, Canada. It is found that the seawater’s conductivity increases with salinity concentration and decreases with sediment concentration. The SSDL developed here can thus be used for both conventional subsea datalogging applications and high turbidity current applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".