Bibliographic record
Abstract
Melanoma is the most dangerous form of skin cancer, and its incidence is increasing significantly among Canadians. In parallel with the rising incidence and morbidity, the financial burden caused by this disease will continue to increase dramatically for the government and for individuals alike. More concerted effort to raise awareness of melanoma in Canada is therefore needed.Risk factors-such as family history, childhood sunburn exposure, and age-play a significant role in an individual's likelihood to develop melanoma. Ultraviolet radiation exposure is the most modifiable variable in melanoma causation. It is therefore important for the general public, in particular the country's youth, to understand the consequences of lifestyle choices-especially tanning bed use and "sun worshipping." Many of these issues are not being addressed fully at either the national or the provincial level, with Canadian efforts trailing those of other nations facing similar challenges. Canada also has workforce issues, with an inadequate distribution and number of physicians who can detect and treat melanoma at an early curative stage. With proper education and public awareness, melanoma prevention can be an achievable goal in Canada.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".