Bibliographic record
Abstract
Skin cancer is the most common form of cancer in Australia, with melanoma, while less common, being the most life threatening form of skin can-cer. In 2005, an estimated 10,014 Australians were diagnosed with melanoma and 1,273 people died, which represented a 41.9 % increase since 1994 (AIHW 2008; The Cancer Council Australia, 2007). Australia and New Zealand have the high-est incidence and mortality rates of melanoma in the world, a rate which is approximately four times higher than that found in Canada, USA and the United Kingdom. (AIHW and AACR, 2004) The primary aetiological factor in the devel-opment of melanoma is UV radiation exposure. Solariums emit stronger UVB rays which are up to five times stronger than the midday sun, with recent studies confirming a significantly higher risk of melanoma among solarium users (Cancer Council Australia, 2007; Gordon and Hirst, 2008). In fact, just one visit to a solarium increases the risk of developing melanoma by 22 per cent, compared with a person who has never used a solarium (Gordon and Hirst, 2008), and use of a solarium before the age of 35 years increases the risk by 75 per cent (IARC Working Group on Artificial Light and Skin Cancer, 2007). However in addition to UV radiation
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.732 | 0.598 |
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".