Güralp Ocean Bottom Monitoring Solutions: Autonomous Nodes, Cabled Observatories and SMART
Bibliographic record
Abstract
Autonomous free-fall OBS units allow users flexibility in deployment and the ability to redeploy in different locations. The Güralp Aquarius functions at any angle without using a gimbal system, and can wirelessly transmit SOH and seismic data to the surface via an integrated acoustic modem. These features allow researchers to monitor and transmit data packets without offshore cabling, reducing logistical challenges whilst maintaining some degree of real-time data transmission. This broad functionality and connectivity has made the Aquarius well-suited for OBS pool use, such as with the National Facility for Seismic Imaging in Canada.Alternatively, cabled solutions provide access to high-resolution data in real time via a physical link to on-shore infrastructure. As an example, the Güralp Orcus provides a complete underwater seismic station with an observatory-grade seismometer and a strong-motion accelerometer in a single package. The slimline Guralp Maris also provides a more versatile solution, using the same omnidirectional sensor as the Aquarius and can be installed either on the seabed or in a narrow-diameter subsea borehole. Both systems are deployed globally as part of multi-disciplinary observatories such as the Neptune array operated by Ocean Networks Canada.SMART cables show great potential for increasing the number of cabled ocean observatory deployments in the future with substantially reduced deployment costs. Combining several applications into a single system, including seismology, oceanography and telecommunications, large scale monitoring networks can be created cost-effectively by combining logistical and fundraising efforts from multiple industries. Güralp is leading the way with a wet demonstration SMART Cable system in the Ionian Sea in collaboration with Instituto Nazionale Di Geofisica e Vulcanologia (INGV) which has proven to be the first practical demonstration of this technology. There are plans for additional projects in the future by leveraging new low-volume and low-power iterations of Güralp sensors and data acquisition modules.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".