A novel GSM and fluorescence coupled full-spectral chlorophyll <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si92.svg"> <mml:mi mathvariant="bold-italic">a</mml:mi> </mml:math> algorithm for waters with high CDM content
Bibliographic record
Abstract
Standard ocean colour algorithms exploiting only shorter visible wavelengths (less than 560 nm) perform poorly in the Arctic Ocean (AO) due to the interference from colored detrital material (CDM). The incorporation of longer wavelengths, which are less susceptible to interference from CDM, could prove beneficial in retrieving water properties, particularly in Arctic waters with high CDM content. Similarly, algorithms that exploit only the red region of the spectrum, such as fluorescence-based approaches, are also unsuitable for these waters. This is due to the difficulty in accurately describing the background elastic scattering signal. In this study, we propose an algorithm that accounts for elastic scattering and fluorescence of phytoplankton in the full visible spectral domain by coupling a tuned version of the Garver-Siegel-Maritorena (GSM) algorithm (GSMA) for the AO with an optimized fluorescence emission model. Our novel algorithm, FGSM, demonstrate comparable overall performance to an empirical algorithm derived for chlorophyll a concentration (Chl) estimates in the AO (AO.emp), with a mean absolute difference (MAD) of 1.83. In addition, FGSM outperforms both the GSMA and the fluorescence line height (FLH) algorithms, with an improvement in the MAD of Chl estimates up to 41 %. Assessments conducted using both in situ datasets and satellite data at the Lena River Delta, a region characterized by high productivity and the presence of coastal CDM, revealed that for eutrophic waters where Chl is generally high, FGSM significantly mitigate the underestimation of Chl by AO.emp and GSMA, and exhibit enhanced robustness to produce more retrievals than the other semi-analytical algorithms. FGSM also demonstrates superior performance compared to the other algorithms assessed in this study for waters with high suspended particulate matter (SPM). Further validations for Arctic waters, particularly turbid coastal waters, are still expected in the future. • A novel full-spectral chlorophyll a algorithm, FGSM, was proposed. • Phytoplankton fluorescence property was considered in the FGSM. • FGSM outperforms other algorithms evaluated for waters with high CDM or SPM. • FGSM was validated very robust for turbid waters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".