Diurnal variation analysis of fluorescence quantum yield during algal blooms based on geostationary ocean color satellite
Bibliographic record
Abstract
Eutrophication in coastal and freshwater environments has led to increasingly frequent and intense algal blooms, posing serious threats to ecosystem health, water quality, and human activities. This study investigates the potential of fluorescence quantum yield (φ) as a dynamic indicator of phytoplankton photosynthetic activity, utilizing satellite observations from the Geostationary Ocean Color Imager (GOCI) and field measurements in Lake Taihu-a eutrophic, hydrodynamically stable lake. Under natural conditions, diurnal variations in φ revealed that phytoplankton dissipates excess energy via non-photochemical quenching (NPQ) at midday light saturation, resulting in reduced φ. These patterns closely reflected photosynthetic dynamics. Building on this foundation, the study analyzed φ dynamics during bloom events in the East China Sea, Ariake Sea, and Sea of Japan. The results demonstrated distinct diurnal patterns related to species composition and local environmental conditions. For example, φ peaked around midday during a dinoflagellate bloom in the East China Sea, while it decreased under intense solar radiation during diatom blooms in the Ariake Sea-highlighting the species-specific photoacclimation strategies. Additionally, a temporal analysis of Chl-a and φ in bloom-affected, nearshore, and offshore waters showed that increases in φ generally preceded Chl-a accumulation, indicating φ's sensitivity as an early warning signal of phytoplankton growth. These findings enhance our understanding of algal bloom dynamics and support the use of φ as a complementary tool for satellite-based bloom monitoring and ecological assessments.
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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".