An Autonomous Underwater Vehicle for the Study of Small Lakes
Bibliographic record
Abstract
A small autonomous underwater vehicle (AUV) was designed and built to carry a wide variety of oceanographic sensors in the relatively benign lacustrine environment. PURLII navigates along preprogrammed paths for up to 3 h using compass, depth, and acoustic altimeter information. A standard, off-the-shelf, self-recording CTD with pump was integrated into PURLII such that vehicle effects on data quality were minimized. An upper bound on temperature data resolution along the vehicle track was estimated to be 10 cm in the vertical and 35 cm in the horizontal. Five missions were conducted over the course of three days in a small lake approaching autumnal turnover. The goal was to obtain several “snapshots” of the temperature structure within the thermocline before and after a wind event. Each mission consisted of the AUV recording CTD data while moving up and down in a vertical sawtooth pattern and following a constant heading. At the end of an allotted period, PURLII would surface, turn through 180°, and repeat the sawtooth pattern while following a return heading to the start point. PURLII was able to complete 27 up and down profiles between 10 and 20 m over 1 km in 50 min. This provided enough temperature data to produce a vertical two-dimensional cross section of the temperature field, 1 km long and 10 m high. Temperature data measured with the AUV-mounted CTD compared favorably with that measured by conventional moored thermistor chains.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".