Identifying the repellent genes in Cannabis (C. sativa) through CRISPR screening. The hidden use of Marijuana
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.
Bibliographic record
Abstract
Chemical pesticides have caused numerous deaths of people, animals, and plants. As a result, alternative pesticides which are health beneficial and ecological are needed. Cannabis sativa, known for its psychoactive effects, can be the solution to this problem. It has excellent repellent characteristics as seen through its use as a companion plant, as well as in-vitro studies. However it has its drawbacks due its controversial nature and lack of research. To solve this problem, our paper aims to locate the non-vital genes in C.sativa that cause its repellent effects (R-genes) through CRISPR screening. To optimally identify the R-genes, the random knocked out genes of C.sativa were compared to the percentage of alive root-knot nematodes (M.incognita) in the plant’s soil. In our experiment, four plants were established per sample: Plant A which is a normal Cannabis sativa, Plant B which is a normal Cannabis sativa being infected by M.incognita, Plant C which is a genetically modified Cannabis sativa, and Plant D which is the same as Plant C except it is being infected by M.incognita. Then the percentage of alive nematodes will be compared in Plant B and D to identify the R genes. The discovery of R-genes is important as it can be used to discover a new class of repellent molecules. They can also be inserted into crops or household plants, giving them Cannabis sativa’s repellent effects, and benefiting agricultural and health fields.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it