Development of a new autonomous underwater moored mobile profiler
Bibliographic record
Abstract
Observation of the ocean environment is key to attaining the knowledge and understanding required to predict its evolving state and to respond to its condition. However, manned observation and collection of high resolution oceanographic data over long periods of time is both expensive and hazardous. Various autonomous profiling platforms have been developed to address the issue but there still exists the opportunity to develop efficient, robust, and reliable, low cost platforms to provide the data. In this paper we present the development of a new autonomous profiling system for areas along the continental shelf. The proposed design is a moored vehicle that will profile the water column by controlling the predetermined ascent/descent routine of an instrumentation payload though the use of a buoyancy engine. One of its unique features is the use of a subsea mooring to secure the profiler while still providing the ability to profile the entire water column and make a telemetry link. The profiler has two modes of dynamic behavior during its profile. One mode is similar to that of a profiler which follows a mooring line, while the other mode is similar to a winched profiler.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".