The Quantitative Effects of Temperature and Light Intensity on Phenolics Accumulation in St. John's Wort ( <i>Hypericum perforatum</i> )
Bibliographic record
Abstract
The quantitative effects of temperature and light intensity on accumulation of phenolics were examined on greenhouse-grown plants of Hypericum perforatum L. Plants were grown in a greenhouse separated into two parts: shaded by 50% transparent polyethylene cover and un-shaded. Temperature values and light intensities were measured daily during the experiment, while plants were harvested weekly for HPLC analyses. Multi regression analyses were performed to describe the quantitative effects of temperature and light intensity on phenolics accumulation. According to the results, increases in temperatures from 24 degrees C to 32 degrees C and light intensities from 803.4 microMm(-2)s(-1) to 1618.6 microMm(-2)s(-1) resulted in a continuous increase in amentoflavone, apigenin-7-glucoside, cholorogenic acid, hyperoside, kaempferol, rutin, quercetin and quercitrin contents. The relationships between temperature, light intensity and phenolics accumulation were formulized as P= [a + (b1 x t) + (b2 x l) + [b3 x(t x l)]] equition, where P is the content of the corresponding phenolic, t temperature (degrees C), l light intensity (microMm(-2)s(-1)) and a, b1, b2 and b3 the coefficients of the produced equation. The regression coefficient (R2) value for amentoflavone was 0.84, for apigenin-7-glucoside 0.87, for cholorogenic acid 0.83, for hyperoside 0.95, for kaempferol 0.76, for rutin 0.70, for quercetin - 0.93, and for quercitrin - 0.86. All R2 values and standard errors of the equations were found to be significant at the p<0.001 level. The mathematical models produced in the present study could be applied by Hypericum researchers as useful tools for the prediction of phenolics content instead of routine chemical analyses.
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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) | 0.001 | 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".