Comparison of iron acquisition from Fe–pyoverdine by strategy I and strategy II plants
Bibliographic record
Abstract
Iron is an essential micronutrient for plants and associated microorganisms. However, the bioavailability of iron in cultivated soils is low. Plants and microorganisms have thus evolved active strategies of iron uptake. Two different iron uptake strategies have been described in dicotyledonous and monocotyledonous graminaceous species. In bacteria, this strategy relies on the synthesis of siderophores. Pyoverdines, a major class of siderophores produced by fluorescent pseudomonads, were previously shown to promote iron nutrition of the dicotyledonous species Arabidopsis thaliana L. (Heynh.), whereas contradictory reports were made on the contribution of those siderophores to the nutrition of graminaceous annuals. Furthermore, no information has so far been available on graminaceous perennials. Here, the contribution of purified pyoverdine of Pseudomonas fluorescens C7R12 to the iron nutrition of two annual and perennial graminaceous plants was assessed and compared with that of two dicotyledonous plant species. Fe–Pyoverdine promoted the iron status of all plant species tested. With the exception of wheat, this promotion was more dramatic in graminaceous species than in dicotyledonous species and was the highest in fescue, a perennial species. The incorporation of 15N-labeled pyoverdine was consistent with the effect on the iron status of the plants tested.
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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".