Changes in light spectra modify secondary compound concentrations and BVOC emissions of Norway spruce seedlings
Bibliographic record
Abstract
Our objective was to study how changes in the light spectra affect growth; the concentration of carbohydrate, chlorophyll, carotenoid, terpene, alkaloid, and phenolic compounds; and the emissions of BVOC (biogenic volatile organic compound) of Norway spruce (Picea abies (L.) Karst.) seedlings. This study was conducted during the growth of the third needle generation in plant growth chambers. The plants were exposed to two light spectra with equal photon flux densities but different proportions of blue light (400–500 nm) produced by LED (light-emitting diode) lamps: (i) control (white light + 12% blue light) and (ii) increased blue light (+B) (white light + 45% blue light). The +B treatment increased the concentrations of total flavonoids and acetophenones in needles. The major changes in the phenolic profile were an accumulation of astragalin derivatives and the aglycone of picein. The +B treatment also decreased concentrations of the main alkaloid compound, epidihydropinidine, and its precursor, 2-methyl-6-propyl-1,6-piperideine; the emission rates of limonene, myrcene, and total monoterpenes; and the concentrations of a few terpenoid compounds, mainly in stems. Growth as well as the carbohydrates and pigments present in the needles were not affected. The results suggest that supplemental blue light shifts carbon allocation between secondary metabolism routes — from alkaloid and terpenoid synthesis to flavonoid and acetophenone synthesis. The changes may affect herbivory and abiotic stress tolerance of Norway spruce.
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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".