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Record W4383894921 · doi:10.1139/cjb-2023-0056

The responses of <i>Cannabis sativa</i> to environmental stress: a balancing act

2023· article· en· W4383894921 on OpenAlexafffundvenue
Josephine Payment, Marina Cvetkovska

Bibliographic record

VenueBotany · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCannabis sativaBiologyCannabinoidCannabisAbiotic componentBiotechnologyEcologyBotanyBiochemistryMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.287
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2023
Admission routes3
Has abstractyes

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