The influence of artificial light at night (ALAN) on algal phenol concentrations can mediate herbivore-alga interactions
Bibliographic record
Abstract
Artificial light at night (ALAN) is a human-induced factor affecting various biological complexity levels and species. Research on ALAN impact has focused on vertebrates and invertebrates, with less attention on primary producers like marine algae. The aim of our study was to evaluate the effect of ALAN on production of phenolic compounds in the red alga, Mazzaella laminarioides, and their indirect impact on the feeding behavior of the marine snail Tegula atra. Algae were exposed to the following treatments: natural day/night cycles, ALAN, and continuous darkness. We observed a higher phenolic concentration during high tide, in according with periods of feeding activity of herbivores. In comparison to algae exposed to natural day/night conditions, those exposed to ALAN showed the lowest concentrations of phenols. Tegula atra consumed significantly more algae than those exposed to ALAN, a result that is consistent with the preference trials, where algae exposed to ALAN was consumed more than algae maintained in natural conditions or continuous darkness. This evidence suggests that ALAN can impact on the production of phenolic compounds and, indirectly, on algal-herbivore interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".