High photoreactivity of chromophoric dissolved organic matter derived from Ulva prolifera and Sargassum
Bibliographic record
Abstract
The epipelagic macroalgae of Ulva prolifera and Sargassum are the primary contributors to widespread seaweed tides globally. Both ocean plants release large amounts of chromophoric dissolved organic matter (CDOM) into the surrounding seawater. The photochemical reactivity of this CDOM, however, has not been adequately addressed. In this study, we extracted CDOM from Ulva prolifera and Sargassum, examined their ultraviolet (UV)-visible absorption characteristics, and quantified their broadband apparent quantum yields (AQY) of absorbance photobleaching and photomineralization (in terms of CO2, CO, and CH4 photoproduction). On a per-unit-weight basis, Sargassum leached 3.5 times more CDOM than did Ulva prolifera in terms of the absorption coefficient averaged over 254–500 nm. Both Ulva prolifera and Sargassum CDOM were characterized by quasi-exponential decay absorption spectra, with Sargassum CDOM exhibiting a distinct shoulder over 310–350 nm suggestive of mycosporine amino acids. The Sargassum CDOM had a higher photobleaching AQY but lower photomineralization AQYs compared to Ulva prolifera CDOM. The photobleaching and photomineralization AQYs of both macroalgal CDOM are, however, orders of magnitude higher than those of CDOM in various natural waters. Potential photoproduction rates of CO2 and CO from the Ulva prolifera CDOM and Sargassum CDOM during the bloom periods are several times to orders of magnitude higher than the air-sea fluxes of these gases in the absence of the macroalgae. This study demonstrates that CDOM released by Ulva prolifera and Sargassum is extremely prone to photobleaching and photomineralization, rendering floating mats of these plants in oceans as potential “hotspots” of greenhouse gas emissions to the atmosphere. This photochemical feedback should be considered when assessing ocean afforestation as a CO2 removal approach to mitigate climate warming.
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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 | 0.000 | 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 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".