Robust Wakeup, Acquisition, and Alignment for Low Cost, Battery-Limited ACOMMS Modems Operating with High Speed Platforms
Bibliographic record
Abstract
Protection of marine mammals, especially “right” whales along the Western Atlantic Ocean, has become a critical imperative for US and Canadian Regulatory agencies and the lobster and crab fishing industries. The historical and continuing practice of deploying sea floor traps connected to surface floats via long rope is one of two primary causes of whale injury and depth. The other is ship strike from speeding vessels. A consensus has been reached within these communities that the ropes must be replaced, but no consensus has yet been reached on precisely how this can be achieved. The Woods Hole Oceanographic Institution (WHOI), has informally developed a concept for the combined use of acoustic communications (acomms) between surface boats and sea floor traps, combined with “cloud-based” connectivity to mitigate mutual interference and noise. The acomms system is based loosely on JANUS [1] in that the modulation follows the precise description developed at CMRE. However, at a center frequency of 25 kHz the wakeup and acquisition in the presence of very high-speed and noisy platforms (up to +/- 7 m/s, or +/-15 kts) requires a significantly different approach.
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 | 0.001 |
| 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 | 0.001 | 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".