ULTRAVIOLET ABSORBING SUBSTANCES FROM <i>AGARUM FIMBRIATUM</i> (PHAEOPHYCEAE) PROTECT <i>GRIFFITHSIA PACIFICA</i> FROM UV RADIATION
Bibliographic record
Abstract
We examined whether naturally occurring polyphenols produced by a brown alga can protect other organisms from ultraviolet radiation. Fragments of Griffithsia pacifica were grown in medium supplemented with a seawater extract of Agarum fimbriatum (3% w/v in seawater for 2 h). Cultures were exposed to cool‐white fluorescent light (7–7.5 μMol photons cm−2 s−1) in the presence or absence of 130 to 168 μW cm−2 UV‐A and 85 to 112 μW cm−2 UV‐B. Regenerating fragments were scored for five qualitative or quantitative measures of growth. Rhizoid growth was not inhibited by the polyphenol extract after ten days. UV radiation in the absence of polyphenols caused considerable cell death and slower growth. Thus, the presence of polyphenols provided protection from UV radiation such that in the highest concentration of polyphenols (1:1 medium:extract), rhizoid growth in UV exposed fragments was approximately 60% as great as control plants without both polyphenols and UV radiation. Plants exposed to UV radiation in the absence of polyphenol extract showed poor rhizoid regeneration and extensive cell death. These experiments document the nature of UV‐A and UV‐B damage to growing red algae, and provide strong evidence that polyphenols from brown algae may have a role in protecting subtidal, benthic algal communities from UV radiation.
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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) | 0.001 | 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".