An empirical ocean color algorithm for estimating the contribution of chromophoric dissolved organic matter to total light absorption in optically complex waters
Bibliographic record
Abstract
To estimate the depth‐integrated rate of photochemical processes involving chromophoric dissolved organic matter (CDOM) in coastal waters, the contribution of CDOM to the total absorption coefficient must be known from UV to green. At 307 sites sampled in various coastal marine environments, the ratio between CDOM and the total absorption coefficient ([ a CDOM /a t ]) at 412 nm was found to vary over a wide range, from 0.20 to 0.95. An empirical algorithm was developed to retrieve [ a CDOM /a t ](412) from satellite remote sensing reflectance. The absolute uncertainty on the [ a CDOM /a t ] retrieval was 0.14. As exemplified with the data from the Baltic and North Seas, the algorithm provides a means to distinguish the contribution of CDOM to the absorption coefficient of colored detrital material (i.e. , CDM = CDOM + nonalgal particles) at the regional scale. The implications of the variability in the magnitude and spectral shape of [ a CDOM /a t ] for the assessment of depth‐integrated production of any photoproducts involving CDOM photolysis are discussed in details. We applied the algorithm to a Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) image of the Southeastern Beaufort Sea where terrestrial inputs are abundant. The spatial variability in the [ a CDOM /a t ] reaches as much as threefold over the continental shelf and beyond. These results clearly show that it is necessary to account for the spatial variability of [ a CDOM /a t ] when quantifying CDOM‐related photochemical processes in the ocean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".