A Brief Update on Current Youth Access Laws NEARLY A QUARTER
Bibliographic record
Abstract
have reported using indoor tanning. The reasons stated for tanning include a desire to achieve a tanned appearance, to socialize and improve their mood, or to relax. 1 Safety concerns surround the addictive nature of tanning, as well as the increased risk for skin cancers, skin See related article aging, and immunosuppression due to the exposure of UV radiation. 2 As a consequence, there has been an alarming rise in melanoma incidence, especially in young women ages 15 to 39 years. 3 Indeed, it has been shown that tanning bed use increases the risk of melanoma by 75% when use occurs before age 35 years. 4 In response to these alarming trends, numerous health and medical organizations have characterized tanning beds as carcinogenic and encouraged increasing state regulations to limit youth access to tanning salons. 4 In February 2012, California became the first state to implement a universal tanning ban for minors
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".