The presence of amino acids affects inorganic N uptake in non-mycorrhizal seedlings of European beech (Fagus sylvatica)
Bibliographic record
Abstract
To investigate the impact of organic N compounds for inorganic nitrogen uptake in the rhizosphere, we fed ammonium nitrate with or without amino acids (i.e., glutamine or arginine) to the roots of non-mycorrhizal beech (Fagus sylvatica L.) seedlings under controlled conditions at different levels of N availability. Uptake of individual N sources was determined from ¹⁵N (inorganic N) and ¹⁵N ¹³C (organic N) accumulation in the roots. In addition, gene fragments encoding proteins involved in N uptake and metabolism were cloned from beech for gene expression analyses by quantitative real-time PCR in the roots. Generally, ammonium was preferred over nitrate as N source. Organic N sources were taken up by beech roots as intact molecules. Uptake of organic N was significantly higher than inorganic N uptake, thus contributing significantly to N nutrition of beech. Depending on the level of N availability, inorganic N uptake was negatively affected by the presence of organic N sources. This result indicates an overestimation of the contribution of inorganic N uptake to N nutrition of beech in previous studies. Apparently, association with mycorrhizal fungi is not essential for organic N uptake by beech roots. Gene expression analyses showed that transcriptional regulation of the amino acid transporters FsCAT3, FsCAT5, FsAAT and FsAAP and the ammonium transporter FsAMT1.2 in the roots is involved in N nutrition of beech.
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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 | 0.001 |
| 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".