SeaCycler: A Moored Open-Ocean Profiling System for the Upper Ocean in Extended Self-Contained Deployments
Bibliographic record
Abstract
Abstract The upper ocean, including the biologically productive euphotic zone and the mixed layer, has great relevance for studies of physical, biogeochemical, and ecosystem processes and their interaction. Observing this layer with a continuous presence, sampling many of the relevant variables, and with sufficient vertical resolution, has remained a challenge. Here a system is presented that can be deployed on the top of deep-ocean moorings, with a drive mechanism at depths of 150–200 m, which mechanically winches a large sensor float and smaller communications float tethered above it to the surface and back down again, typically twice per day for periods up to 1 year. The sensor float can carry several sizeable sensors, and it has enough buoyancy to reach the near surface and for the communications float to pierce the surface even in the presence of strong currents. The system can survive mooring blowover to 1000-m depth. The battery-powered design is made possible by using a balanced energy-conserving principle. Reliability is enhanced with a drive assembly that employs a single rotating part that has no slip rings or rotating seals. The profiling bodies can break the surface to sample the near-surface layer and to establish satellite communication for data relay or reception of new commands. An inductive pass-through mode allows communication with other mooring components throughout the water column beneath the system. A number of successful demonstration deployments have been completed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".