Khat: A Boon or bane to humanity
Bibliographic record
Abstract
Khat, an evergreen shrub habitually ingested for its euphoric and \nstimulatory effects, is grown mainly in eastern Africa and south-western \nArabia. Its consumption has, for a long time, been restricted to areas \nclose to the sites of production but, because of recent improvement in \nefficiency and speed of transportation, khat consumption has spread to far \nflung lands such as North America, Canada, Australia, United Kingdom \nand parts of Europe. In regions where it is grown, daily life seems to \ncentre on the crop bringing together farmers, traders, middle men, \nconsumers and transporters. Thousands of dollars change hands daily in \nmarket centres where khat business is transacted making it the most \nlucrative business in these regions. The economies of these regions are \ntherefore driven and sustained by khat trade. Khat contains a psychoactive \ncompound belonging to the phenylpropylamine group of alkaloids called \ncathinone which has amphetamine-like effects. It causes mild euphoria, \nwakefulness and a host of other effects on regular consumers. Long distance \ntruck drivers are known to constantly chew khat in order to stay awake. \nSome chewers, however, derive satisfaction in the ability of khat to promote \nwork endurance while others do it for leisure. Consumers generally prefer \nyoung leaves and shoots because they contain a more potent active \ningredient cathinone which decays within 48 h to a less potent form called \ncathine. Despite these positive attributes of khat, it poses serious health \nrisks to consumers arising from the effects of its active ingredient, cathinone,
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.177 | 0.079 |
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".