Passive symplastic phloem loading in the duckweed <i>Spirodela polyrhiza</i>
Bibliographic record
Abstract
Abstract The lemnoidae, commonly called duckweeds, are a group of small, rapidly growing aquatic plants that play an important role in pond ecosystems and are used in biotechnological and remediation applications. While small, duckweeds feature phloem tissue in fronds and roots. To gain insight on duckweed phloem function, we investigated how sugar is loaded into the phloem sieve elements in the giant duckweed Spirodela polyrhiza . Genomes of S. polyrhiza and three other duckweeds do not feature genes for the sucrose transporters typically associated with active apoplastic phloem loading. Neither did a sucrose transporter inhibitor affect sucrose concentration in phloem exudate. Active symplastic phloem loading was excluded based on the conventional plasmodesmata configuration and absence of oligosaccharides in S. polyrhiza phloem. Instead, uniform plasmodesmata density along the phloem loading pathway indicated a passive symplastic phloem loading type. When plasmodesmata permeability was artificially reduced by hormone treatment, the sucrose concentration in the phloem exudate was reduced, highlighting the potential role of plasmodesmata regulation in setting carbon export rates in species with passive phloem loading. Our results identify S. polyrhiza as the first monocot species with passive phloem loading. Moreover, they indicate opportunities for optimization of duckweed growth.
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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) | 0.000 | 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".