Bibliographic record
Abstract
There is an illness that afflicts about 17.6 million American adults each year.In the U.S., it is the number one reason that someone consults a family physician.It costs the economy more than ulcers, diabetes, arthritis or hypertension.What is this mysterious illness?--It is depression.Depression has been treated in the past with prescription drugs such as Prozac, Zoloft, and Paxil, but now more and more people are turning to the herbal "remedy" known as St. John's wort (3).St. John's wort, also known as Hypericum perforatum, has grown in popularity in the last several years.Its' popularity originated in Europe where it is prescribed and treated as a drug (5).In Germany, St John's wort extract is prescribed 8 times more often than Prozac for depression (7).In the United States, retail sales of St. Johns wort climbed by almost 3,000% during the past year (5)!This herbal is effective for mild to moderate depression and can also help those who have troubles sleeping (2).Even though St. John's wort seems like the perfect remedy for mild to moderate depression, there can be drug interactions and side effects associated with it.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.037 |
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".