Modeling the short wavelength infrared laser radiance reflection on the sea surface
Bibliographic record
Abstract
The sea surface is a complex dynamic structure dependent on atmospheric conditions, and for which physical and chemical properties change from water to foam. Its roughness determines how the surface reflects, absorbs, and emits radiance, and depends on multiple parameters such as wind speed and direction, and foam and turbulence induced from natural waves or from object displacement. In this paper, a model description is given for laser reflection on the sea surface in open water driven by the wind. The model allows calculation of the reflected laser radiance from the sea surface toward a receiver as a function of the incoming laser radiance with a known beam intensity profile. Each subarea of the sea surface seen by one pixel of the receiver is considered as an ensemble of facets, where each facet is defined by its x and y directional slopes. The wind speed and orientation determine the probability density function of the sea surface facet slope occurrence. In this paper, we have analytically expressed the reflected radiance on the sea surface as a function of the wind speed, receiver range, receiver heading, laser position, laser output aperture, and laser incoming radiance. Using the tolerance ellipse, the reflected radiance expression was approximated, and both direct and approximated results were compared. The richness in behavior of the reflected radiance and its dependence on the geometry of the problem were studied showing the impact of the receiver position, the laser position, heading, and beam divergence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".