New developments in submarine cable technology can facilitate acoustics in Polar regions and on the global scale
Bibliographic record
Abstract
The subsea telecommunications cable industry is expanding their present single purpose infrastructure to include ocean observing capability. Science Monitoring And Reliable Telecommunications (SMART) Subsea Cables is working to integrate temperature, pressure, and seismic acceleration sensors into commercial cables (∼70 km spacing) to support climate and ocean observation, sea level monitoring, and tsunami and earthquake early warning on the global scale. Furthermore, telecom rated branching cables with power feed units supporting multipurpose “nodes” are becoming a reality. Acoustic capability can be integral to both. Major uses of these nodes include supporting low frequency transceivers enabling basin scale tomography and geo-positioning of mobile assets and docking for autonomous undersea vehicles (AUVs). Enabled by cabled power, these would be part of the fixed/mobile acoustic tomography system measuring ocean heat content at the speed of sound and more generally for transporting energy, data, and acquiring multidisciplinary data throughout a large volume of the ocean. Both variants and hybrids between can support the necessary acoustics contribution to ocean observing. Two proposed systems can support polar applications: Far North Fiber Express connecting Norway/Finland/Ireland with Japan via the Canadian Northwest Passage, and the NSF proposed SMART cable connecting New Zealand with McMurdo Base, Antarctica.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".