A Validated Low-LOQ Study of Cannabinoid Content in Cold-Pressed Hemp Seed Oil (CPHSO) Manufactured in North America
Bibliographic record
Abstract
A study of cannabinoid content in commercially available cold-pressed hemp seed oil (CPHSO) manufactured in North America and assayed using a validated low-LOQ analytical method with UHPLC-MS/MS quantitation was conducted. Thirty CPHSO samples from small, medium, and large-scale manufacturers were voluntarily submitted. Samples were produced from eleven known cultivars grown in three Canadian provinces and six US States, plus one sample from seeds imported from Poland and pressed in the USA. Oil density was measured for each sample, as were the content of sixteen cannabinoids with validated commercial reference standards, and reported in parts-per-million (ppm). Observational and statistical methods were used to examine variances in analyte concentrations, demonstrating significant differences in cannabinoid concentrations between samples. Several per-sample and per-analyte heatmaps aided in the visual examination of variances. A two-phase series of linear regressions were performed on normally distributed cannabinoids with raw and trimmed data sets to determine if content variations correlated to manufacturer cleaning, handling, and storage procedures, or if the variation was influenced more by cultivar. The research findings suggest that variance in cannabinoid content is likely most influenced by cultivar, but do not rule out contributions by supplier handling and processing techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".