Temporal patterns of fucoxanthin in four species of European marine brown macroalgae
Bibliographic record
Abstract
Brown seaweeds are a rich source of carotenoids, particularly fucoxanthin, which has a wide range of potential health applications. Fucoxanthin fluctuates within and among seaweeds over time, frustrating efforts to utilise this resource. Thus, we require comprehensive analyses of long- and short-term concentrations across species in field conditions. Here, we used High Performance Liquid Chromatography to compare fucoxanthin content in four brown macroalgae, Ascophyllum nodosum, Fucus serratus, Fucus vesiculosus and Saccharina latissima, monthly for 1 year. F. serratus and F. vesiculosus had significantly higher fucoxanthin content (mg/g), which was highest in Spring (0.39 ± 0.04) and Autumn (0.45 ± 0.04) [mean (± SE)]. Two species, A. nodosum and F. serratus, were collected monthly at the same location for a further two non-consecutive years. For both A. nodosum and F. serratus, a significant interaction effect of seasons and years was identified, highlighting that there is variation in fucoxanthin content among and within species over time. We also show that fucoxanthin content differs significantly among months even within seasons. Therefore, it is not sufficient to assess fucoxanthin in single months to represent seasonality. We discuss how weather, nutrients and reproduction may have driven the seasonal variation, and reveal patterns of fucoxanthin concentration that can provide information concerning its availability for many important medical functions.
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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 | 0.001 | 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".