Variability of total THC in greenhouse cultivated dried Cannabis
Bibliographic record
Abstract
Natural variation in secondary metabolites is commonplace in organic matrices, including consumable fruits and vegetables. However, absolute concentration and variation of secondary metabolites are generally not of concern to consumers. Cannabis sativa and C. indica dried cannabis total Delta-9 Tetrahydrocannabinol (THC) is of importance to consumers/patients and must be accurately reported for both recreational and medical dried flower. In this report, the variation in Total THC in THC-dominant commercially relevant cultivars was investigated. The variation in Total THC values within different strata of the plant and between plants of the same batch were explored using a single analytical method. Within one stratum across nine batches (n = 27-57), Total THC varied by 3.1-6.7% of actual content, with only ~ 30-41% of individual replicates falling within their respective 99% confidence internal (CI) (representative of the batch mean). Between the top and bottom of plants across three batches, Total THC varied by 4.7-6.1% of actual THC content. Between plants of one cultivar, average Total THC varied by 2.8% which was statistically significant (p < 0.0001). Effect size (ES) measures were also reported for plant strata and plant against plant analysis. A comprehensive analysis of the extent of Total THC variation in samples of dried cannabis, evident both within and across plants of the same batch, has been presented using a random sampling and sample size calculated approach. Herein we demonstrate the natural variability present in dried cannabis flower using a single analytical method.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".