The responses of <i>Cannabis sativa</i> to environmental stress: a balancing act
Bibliographic record
Abstract
Cannabis sativa L. is one of the oldest cultivated crops, used for its fiber and medicinal properties. The cannabis plant synthesizes a myriad of secondary metabolites, but the most valuable products from a medical and commercial standpoint are cannabinoids. Despite significant advances in elucidating the biochemistry and genetics that govern cannabinoid accumulation, we still do not have conclusive evidence for the role of these secondary metabolites in the physiology of C. sativa. In line with known functions of other secondary metabolites, the protective functions of cannabinoids against temperature stress, poor micronutrient soil content, drought, ultraviolet B radiation, and as antimicrobial agents have been suggested, but are yet to be conclusively demonstrated. Recent research suggests that the environment has a major effect on cannabis growth and productivity, but the relationship between stress, cannabinoid accumulation, and plant health is complex. Here, we summarize the current insights on how abiotic and biotic stresses affect C. sativa biology. We also examine the available evidence to support the hypothesis for the protective function of cannabinoids against environmental stressors. Maintaining optimal growth and high cannabinoid synthesis is a balancing act, one that can only be achieved by better understanding of the effects of the environment on the cannabis plant.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".