Autonomous underwater vehicle (AUV) mapping reveals coral mound distribution, morphology, and oceanography in deep water of the Straits of Florida
Bibliographic record
Abstract
To make progress in understanding the distribution and genesis of coral mounds in cold and dark water, maps of morphology and oceanographic conditions resolving features at the 1–10 m scale are needed. An autonomous underwater vehicle (AUV) cruising 40 m above the seafloor surveyed a 48 km 2 coral mound field in 600–800 m water depth at the base of slope of Great Bahama Bank. The AUV acquired 1–3 meter resolution acoustic backscatter and bathymetry together with current vectors, salinity, and temperature. The multibeam bathymetry resolved more than 200 coral mounds reaching up to 90 m height. Mound morphology is surprisingly diverse and mound distribution follows E‐W oriented off‐bank ridges. Bottom currents reverse every 6 hours indicating tidal flow decoupled from the north flowing surface current. The AUV data fill the gap between low‐resolution surface‐based mapping and visual observations on the seafloor, revealing the dynamic environment and spatial relationships of an entire coral mound field.
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 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 | 0.000 |
| 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 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".