Optical influence of ship wakes
Bibliographic record
Abstract
The optical variations observed within ship wakes are largely due to the generation of copious amounts of air bubbles in the upper ocean, a fraction of which accumulate as foam at the surface, where they release scavenged surfactants. Field experiments were conducted to test previous theoretical predictions of the variations in optical properties that result from bubble injection in the surface ocean. Variations in remote-sensing reflectance and size distribution of bubbles within the ship-wake zone were determined in three different optical water types: the clear equatorial Pacific Ocean, moderately turbid coastal waters, and very turbid coastal waters, the latter two of which were offshore of New Jersey. Bubbles introduced by moving vessels increased the backscattering in all cases, which in turn enhanced the reflectance over the entire visible and infrared wave bands. The elevated reflectance had different spectral characteristics in the three locations. The color of ship wakes appears greener in the open ocean, whereas little change in color was observed in near-coastal turbid waters, consistent with predictions. Colorless themselves, bubbles increase the reflected radiance and change the color of the ocean in a way that depends on the spectral backscattering and absorption of the undisturbed background waters. For remote observation from aircraft or satellite, the foam and added surfactants further enhance the reflectance to a degree dependent on the illumination and the viewing geometry.
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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".