Measurements of near-surface bubble plumes in the open ocean with implications for high-frequency sonar performance
Bibliographic record
Abstract
This study examines near-surface bubble data obtained with a self-contained 200-kHz inverted echo-sounder deployed at Ocean Station Papa (NE Pacific, 1400 km west of Vancouver Is.) over an 81-day period in the spring of 1996. The instrument operated continuously, recording calibrated volume scattering profiles from near-surface bubbles with 3-s and 30-cm resolution. The data show the frequent occurrence of bubbles organized into vertical, plume-like structures, presumably drawn downwards by turbulence and other near-surface circulations. Average bubble plume penetrations of up to 15 m were observed, with maximum penetrations up to 25 m. Within the plumes, the backscatter cross section exhibited an exponential decay with depth, with e-folding scale in the range 0.5 to 3 m, increasing proportionally to the square of average plume depth. Using standard models for bubble scattering, and incorporating recent acoustic resonator measurements of bubble-size distributions along with actual bubble plume data, high-frequency near-surface sonar performance models were developed. These models show that on a ping-to-ping basis the bubble plume structures can induce significant spatial variations in the reverberation level and path-integrated extinction losses to near-surface targets.
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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 | 0.001 |
| 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 | 0.000 | 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".