Mobile Distributed Temperature Sensing of the Air/Water Interface of an Aquatic Environment with an Unmanned Surface Vehicle
Bibliographic record
Abstract
Aquatic habitats have a boundary layer near the air–water interface (AWI) that governs mass transport. Little is known about temperature profiles and boundary layers at the AWI. We used a high-resolution distributed temperature sensing (HR-DTS) system onboard an unmanned surface vehicle (USV) to resolve temperature profiles from about 1 m above and 1 m below the surface of the water. Our USV–HR-DTS system resolved a temperature differential of about 5.5 °C at the AWI, spanning a distance of approximately 13 cm. DTS profiles were similar for stationary holds and forward and reverse transects in the water. There was a significant change in temperature as a function of height, with an exponential decrease in temperature starting around 13 cm down to the AWI (P = 2 × 10−16). This is the first application of a HR-DTS onboard a USV to examine temperature profiles across the AWI. To our knowledge, these are the first high-resolution temperature profiles of the AWI captured from a mobile platform. Because our USV–HR-DTS system is mobile, it could be used to profile temperatures at the AWI at multiple locations in a large body of water. This technology could also find unique applications in the measurement of meteorological drivers of hazardous agent dispersal for source localization efforts.
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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 | 0.000 |
| 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".