Retrieval of phytoplankton biomass from simultaneous inversion of reflectance, the diffuse attenuation coefficient, and Sun‐induced fluorescence in coastal waters
Bibliographic record
Abstract
A model has been developed to retrieve phytoplankton absorption, a proxy for phytoplankton biomass, from observations of reflectance (R) and the diffuse attenuation coefficient (Kd) collected by moored radiometers in coastal waters, where high concentrations of chromophoric dissolved organic matter (CDOM) confound conventional ocean color algorithms. The inversion uses simultaneously two forward models: (1) a look‐up table (LUT) that accounts for instrument geometry and the effect of the solar angle on both R and Kd and (2) an analytical function describing the effects of Sun‐induced fluorescence (SIF) of chlorophyll on R. Estimates of phytoplankton biomass (mostly from SIF), the absorption by colored matter, and the particulate backscattering coefficients (mostly from the LUT) are obtained by optimizing the amplitude and shape of the absorption and backscattering coefficients in the forward model to best match the observations. An equation describing the quantum yield of fluorescence (photons fluoresced/photons absorbed) as a function of incident irradiance constrains the model and allows estimates of phytoplankton absorption. Innovations include: the utilization of both R and Kd, providing good separation of the effects of backscattering from absorption; avoidance of reflectance bands between 400 and 600 nm, thereby avoiding interference from bottom reflection and CDOM fluorescence; utilization of the full emission band of SIF; and accounting for the irradiance‐dependence of its quantum yield. The model effectively retrieved chlorophyll concentration from an independent data set — r = 0.76, n = 93, with a mean absolute percent error (MAPE) of 24%; this is better than a modern ocean color algorithm (OC4V4) on its validation data set, when restricted to the same range of chlorophyll (r = 0.67, n = 384, MAPE = 51%).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".