Bibliographic record
Abstract
Article Source: http://www.popsci.com/science/article/2012-11/fyi-can- viagra-make-you-a-better-athlete Introduction: Athletes have started taking Viagra to gain a competitive edge. Viagra’s use might be expanding into the athletic world for its alleged boost to one’s physical capability. This paper will discuss Viagra’s impact on society, its users, athletic organisations, and whether its purported performance enhance- ment uses are valid and safe. Pharmacology: Viagra works by relaxing smooth muscle cells through the in- hibition of PDE5, thus, increasing the bioavailability of cGMP. Results: Subjects had a higher VO2 max and recovered faster from pulmonary hypertension when given Viagra. Other results are mixed and not well estab- lished. Barriers: Allowing Viagra into the athletic community may cause concern from sports organisations but will conversely and unequally burden those who require Viagra for healthy sexual function. Conclusion: These findings do not directly correlate to an improvement in ath- letic performance. Furthermore, taking Viagra may pose as a risk to one’s health. Viagra should only be taken upon consultation with a health care professional.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".