Colors of Macroalgae: Distinguishing <i>Ulva Prolifera</i> and <i>Sargassum Horneri</i> Using Sentinel-2 L2A Images
Bibliographic record
Abstract
Macroalgae blooms have occurred in the East China Sea (ECS) and Yellow Sea (YS) frequently in recent years. Two of the most common macroalgae are mainlyUlva proliferaandSargassum horneri. In this article, a novelty method named commission internationale de l'éclairage (CIE) color space of macroalgae (CIE-M) was developed to distinguishUlva proliferaandSargassum horneriusing Sentinel-2 L2A data. First, sinceUlva proliferaandSargassum hornerihave biologically different pigment compositions, the color difference between them was plotted in the CIE color space using data extracted from the Sentinel-2 L2A images. It was found that most of the pixels fromUlva proliferashowed higher CIE_xand CIE_ythan those of theSargassum horneri. For practical application, thresholds of 0.345 and 0.35 were chosen for simple binary classification betweenUlva proliferaandSargassum hornerion satellite images. Second, the developed CIE-M method was compared with the other three previous methods, green algae index, slope of red–green, and Sargassum andUlva proliferaindex. The results showed that our newly developed method has satisfactory extraction results not only in clear region, but also in cloudy, and turbid region. Finally, the monthly spatial variation ofUlva proliferaandSargassum horneriduring 2019–2023 was analyzed separately, using the Sentinel-2 L2A images. It was found thatUlva proliferamainly occurred in the YS from late spring to summer seasons, whileSargassum horneriblooms mainly occurred during winter to spring in the ECS and YS, with maximum area in February.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".