High Resolution Spatiotemporal Time Series of Water Column Structure Data Acquired with Autonomous Operated Profiling CTD Mounted on a Viking Met-Ocean Buoy in Support of Autonomous Vehicle Test Area
Bibliographic record
Abstract
A 2-meter (79 in) diameter and 4.5-meter (180 in) tall Viking buoy, built by the Canadian company MTE Instruments, served as offshore platform in the operation of an RBR Concerto CTD connected to an autonomously operated profiling winch. The CTD was attached to MTE Instrument's Mini Winch to record time series data of the water column's physical properties from surface to seafloor at 20m depth in support of an autonomous vehicle test area. Data acquisition and power management of the winch were handled by the main buoy controller. Meteorological and oceanographic sea state conditions for operation of the winch were monitored in real time by the winch and buoy controllers to ensure safe deployment and recovery of the CTD into the water from its in-hull storage tube, thus avoiding entanglement with the anchor chain. Storage of the CTD in a hull mounted tube above the water line kept the CTD sensors from biofouling. Full time series data were stored on the buoy and data snippets were transmitted to shore in near real time via iridium data burst. These data were subsequently quality controlled and are publicly available from NOAA (National Oceanic & Atmospheric Administration) NDBC (National Data Buoy Center) data servers. Here we report on the autonomously collected CTD and ADCP (Acoustic Doppler Current Profiler) data collected during that deployment.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".