Selenium accumulation in flowers and its effects on pollination
Bibliographic record
Abstract
• Selenium (Se) hyperaccumulation has a profound effect on plant-arthropod interactions. Here, we investigated floral Se distribution and speciation in flowers and the effects of floral Se on pollen quality and plant-pollinator interactions. • Floral Se distribution and speciation were compared in Stanleya pinnata, an Se hyperaccumulator, and Brassica juncea, a comparable nonhyperaccumulator. Pollen germination was measured from plants grown with varying concentrations of Se and floral visitation was compared between plants with high and low Se. • Stanleya pinnata preferentially allocated Se to flowers, as nontoxic methyl-selenocysteine (MeSeCys). Brassica juncea had higher Se concentrations in leaves than flowers, and a lower fraction of MeSeCys. For B. juncea, high floral Se concentration impaired pollen germination; in S. pinnata Se had no effect on pollen germination. Floral visitors collected from Se-rich S. pinnata contained up to 270 μg g(-1), concentrations toxic to many herbivores. Indeed, floral visitors showed no visitation preference between high- and low-Se plants. Honey from seleniferous areas contained 0.4-1 μg Se g(-1), concentrations that could provide human health benefits. • This study is the first to shed light on the possible evolutionary cost, through decreased pollen germination in B. juncea, of Se accumulation and has implications for the management of seleniferous areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".