Total phenolics and individual phenolic acids vary with light environment in<i>Lindera benzoin</i>
Bibliographic record
Abstract
The understory shrub Lindera benzoin L. experiences lower rates of herbivory in sun environments than in shade environments. The production of secondary metabolites (e.g., phenolic compounds with known plant defense properties) is one likely contributor to these observed differences in herbivory. This work determined the total phenolic content as well as the concentrations of several individual phenolic acids in L. benzoin leaves found in sun and shade habitats. Total phenolic concentrations were determined to be higher in leaves from sun plants than in those from shade plants (47.5 ± 2.4 vs. 28.6 ± 1.3 gallic acid equivalents, respectively). High-performance liquid chromatography with diode array detection was used to separate and quantify several individual phenolic acids, and specific compounds were identified based on their retention times and ultraviolet spectra. The concentrations of vanillic, chlorogenic, p-coumaric, and ferulic acids were shown to be statistically higher in leaves from sun plants than in those from shade plants (P < 0.05), whereas 2,5-dihydroxybenzoic acid and caffeic acid were not significantly different in L. benzoin leaves from sun versus shade habitats.
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) | 0.001 | 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".