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Record W4296005787 · doi:10.3390/plants11182383

New Insight into Ornamental Applications of Cannabis: Perspectives and Challenges

2022· article· en· W4296005787 on OpenAlex
Mohsen Hesami, Marco Pepe, Austin Baiton, Seyed Alireza Salami, Andrew Maxwell Phineas Jones

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlants · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrnamental plantCannabisLandscapingHabitBiologyLegalizationHerbariumBotanyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The characteristic growth habit, abundant green foliage, and aromatic inflorescences of cannabis provide the plant with an ideal profile as an ornamental plant. However, due to legal barriers, the horticulture industry has yet to consider the ornamental relevance of cannabis. To evaluate its suitability for introduction as a new ornamental species, multifaceted commercial criteria were analyzed. Results indicate that ornamental cannabis would be of high value as a potted-plant or in landscaping. However, the readiness timescale for ornamental cannabis completely depends on its legal status. Then, the potential of cannabis chemotype Ⅴ, which is nearly devoid of phytocannabinoids and psychoactive properties, as the foundation for breeding ornamental traits through mutagenesis, somaclonal variation, and genome editing approaches has been highlighted. Ultimately, legalization and breeding for ornamental utility offers boundless opportunities related to economics and executive business branding.

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.

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 score0.615
Threshold uncertainty score0.398

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 head0.022
GPT teacher head0.290
Teacher spread0.267 · 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