Polarized light field under dynamic ocean surfaces: Numerical modeling compared with measurements
Bibliographic record
Abstract
As part of the Radiance in a Dynamic Ocean (RaDyO) program, we have developed a numerical model for efficiently simulating the polarized light field under highly dynamic ocean surfaces. Combining the advantages of the three‐dimensional Monte Carlo and matrix operator methods, this hybrid model has proven to be computationally effective for simulations involving a dynamic air‐sea interface. Given water optical properties and ocean surface wave slopes obtained from RaDyO field measurements, model‐simulated radiance and polarization fields under a dynamic surface are found to be qualitatively comparable to their counterparts from field measurements and should be quantitatively comparable if the light field measurement and the wave slope/water optical property measurements are appropriately collocated and synchronized. This model serves as a bridge to connect field measurements of water optical properties, wave slopes and polarized light fields. It can also be used as a powerful yet convenient tool to predict the temporal underwater polarized radiance in a real‐world situation. When appropriate surface measurements are available, model simulation is shown to reveal more dynamic features in the underwater light field than direct measurements.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".