Bibliographic record
Abstract
Teen Vaping Doubles in the United StatesSince 2017, the rate of e-cigarette use in teens has doubled in the US.In 2019, 25% of twelfth graders, 20% of tenth graders, and 9% of eighth graders reported having vaped nicotine in the past month.This increase is attributed to the variety of appealing flavours and perception of safety of vaping.The first death from e-cigarettes occurred in September 2019 and, since then, over 1479 cases with 33 deaths have been reported.The injuries are typically chemically induced and have been largely attributed to illicit vaping liquids and vaping products containing THC.While the majority of cases have been in the US, an increasing number of cases have been reported in Canada. September 2019 -United States of America Dengue Fever Stings in HondurasDengue is a viral infection that causes causes high fever and joint pain.It can develop into a potentially lethal complication called severe dengue.Over 40,000 cases and 135 deaths have been reported in Honduras in 2019, marking the worst outbreak of the virus in over 50 years.In comparison, only 8,000 cases were reported in 2018.The increase has led Honduras's government to declare a national emergency and fumigate the breeding grounds of yellow fever mosquitoes which spread the disease.Experts speculate that climate change and Honduras's three-month rainy season may have contributed to the epidemic.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.836 | 0.720 |
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".