Environmental Factors Regulate Plant Secondary Metabolites
Bibliographic record
Abstract
Abiotic environmental stresses can alter plant metabolism, leading to inhibition or promotion of secondary metabolites. Although the crucial roles of these compounds in plant acclimation and defense are well known, their response to climate change is poorly understood. As the effects of climate change have been increasing, their regulatory aspects on plant secondary metabolism becomes increasingly important. Effects of individual climate change components, including high temperature, elevated carbon dioxide, drought stress, enhanced ultraviolet-B radiation, and their interactions on secondary metabolites, such as phenolics, terpenes, and alkaloids, continue to be studied as evidence mounting. It is important to understand those aspects of secondary metabolites that shape the success of certain plants in the future. This review aims to present and synthesize recent advances in the effects of climate change on secondary metabolism, delving from the molecular aspects to the organismal effects of an increased or decreased concentration of these compounds. A thorough analysis of the current knowledge about the effects of climate change components on plant secondary metabolites should provide us with the required information regarding plant performance under climate change conditions. Further studies should provide more insight into the understanding of multiple environmental factors effects on plant secondary metabolites.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".