Causal relationship between growth inhibition, accumulation of phenolic metabolites, and changes of UV-induced fluorescences in nitrogen-deficient barley plants
Bibliographic record
Abstract
UV-induced chlorophyll (ChlF UV ) and blue-green (BGF) fluorescences emitted by leaves have been proposed as useful indicators of plant physiological status under stress conditions. In this report, we investigated the effects of nitrogen (N) deficiency on ChlF UV and BGF emissions of leaf sections in relation to plant growth inhibition and accumulation of phenolic metabolites in barley leaves. Plants in a growth chamber were supplied with one of five different NO 3 concentrations (0.8, 1.6, 5.0, 8.0, or 16 mmol/L) for a 2-week period. Total fresh biomass decreased with decreasing N supply, whereas leaf content of soluble phenolic compounds increased. This increase in leaf phenolic compounds observed with limiting N supply was accompanied by large increases of the BGF intensity and BGF/ChlF UV ratio of leaf sections and, to a lower extent, by a decrease of the leaf epidermal transmittance of UV radiation (as estimated by the ratio of ChlF intensities induced by UV and blue excitation). Interestingly, we observed a negative correlation (r 2 = 0.975) between fresh biomass of plants and BGF intensity of leaf sections. By interpreting, in a context of stress physiology, the changes of UV-induced fluorescences, our results support their potential use as a sensitive tool for the rapid assessment of a plant's nutrient status.Key words: blue-green fluorescence, chlorophyll fluorescence, flavonoids, growth inhibition, nitrogen deficiency, phenolic compounds.
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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".