Leaf mineral nutrient remobilization during leaf senescence and modulation by nutrient deficiency
Bibliographic record
Abstract
Higher plants have to cope with fluctuating mineral resource availability. However, strategies such as stimulation of root growth, increased transporter activities, and nutrient storage and remobilization have been mostly studied for only a few macronutrients. Leaves of cultivated crops (Zea mays, Brassica napus, Pisum sativum, Triticum aestivum, Hordeum vulgare) and tree species (Quercus robur, Populus nigra, Alnus glutinosa) grown under field conditions were harvested regularly during their life span and analyzed to evaluate the net mobilization of 13 nutrients during leaf senescence. While N was remobilized in all plant species with different efficiencies ranging from 40% (maize) to 90% (wheat), other macronutrients (K-P-S-Mg) were mobilized in most species. Ca and Mn, usually considered as having low phloem mobility were remobilized from leaves in wheat and barley. Leaf content of Cu-Mo-Ni-B-Fe-Zn decreased in some species, as a result of remobilization. Overall, wheat, barley and oak appeared to be the most efficient at remobilization while poplar and maize were the least efficient. Further experiments were performed with rapeseed plants subjected to individual nutrient deficiencies. Compared to field conditions, remobilization from leaves was similar (N-S-Cu) or increased by nutrient deficiency (K-P-Mg) while nutrient deficiency had no effect on Mo-Zn-B-Ca-Mn, which seemed to be non-mobile during leaf senescence under field conditions. However, Ca and Mn were largely mobilized from roots (-97 and -86% of their initial root contents, respectively) to shoots. Differences in remobilization between species and between nutrients are then discussed in relation to a range of putative mechanisms.
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) | 0.000 | 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".