High stability ferric chelates result in decreased iron uptake by the green alga<i>Chlorella kessleri</i>owing to decreased ferric reductase activity and chelation of ferrous iron
Bibliographic record
Abstract
Cells of the green alga Chlorella kessleri Fott et Nováková use a reductive mechanism for iron acquisition. Iron-limited cells acquired iron more rapidly from a chelator with a lower stability constant for Fe3+(hydroxyethylethylenediaminetriacetic acid (HEDTA)) than from a chelator with a higher stability constant (N,N′-di[2-hydroxybenzyl)ethylenediamine-N,N′-diacetic acid (HBED)). Furthermore, iron uptake rates decreased with increasing chelator concentrations at constant iron concentration. The negative effects of elevated HBED levels on iron uptake could be partly alleviated by the addition of Ga3+, which suggests that iron-free chelator has a negative effect on iron acquisition by competing for Fe2+with the ferrous transport system. Furthermore, ferric reductase activity progressively decreased with increasing concentrations of both chelators (in the iron-free form). This effect was not alleviated by Ga3+addition and was apparently caused by the direct inhibition of the reductase. Overall, we conclude that chelators with high stability constants for Fe3+decrease iron acquisition rates by Strategy I organisms via three separate mechanisms.
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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".