An Autonomous Underwater Vehicle for the Study of Small Lakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it