Penetration of UV‐visible solar radiation in the global oceans: Insights from ocean color remote sensing
Bibliographic record
Abstract
Penetration of solar radiation in the ocean is determined by the attenuation coefficient (Kd(λ)). Following radiative transfer theory, Kd is a function of angular distribution of incident light and water's absorption and backscattering coefficients. Because these optical products are now generated routinely from satellite measurements, it is logical to evolve the empirical Kd to a semianalytical Kd that is not only spectrally flexible, but also the sun‐angle effect is accounted for explicitly. Here, the semianalytical model developed in Lee et al. (2005b) is revised to account for the shift of phase function between molecular and particulate scattering from the short to long wavelengths. Further, using field data collected independently from oligotrophic ocean to coastal waters covering >99% of the Kd range for the global oceans, the semianalytically derived Kd was evaluated and found to agree with measured data within ∼7–26%. The updated processing system was applied to MODIS measurements to reveal the penetration of UVA‐visible radiation in the global oceans, where an empirical procedure to correct Raman effect was also included. The results indicated that the penetration of the blue‐green radiation for most oceanic waters is ∼30–40% deeper than the commonly used euphotic zone depth; and confirmed that at a depth of 50–70 m there is still ∼10% of the surface UVA radiation (at 360 nm) in most oligotrophic waters. The results suggest a necessity to modify or expand the light attenuation product from satellite ocean‐color measurements in order to be more applicable for studies of ocean physics and biogeochemistry.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".