Bibliographic record
Abstract
Users can access data about cancer statistics in the United States and Canada including but not limited to searches by type of cancer, race, sex, population, and geography. Background The Cancer in North America database is maintained by the North American Association for National Cancer Registries. The North American Association for National Cancer Registries partners with the Centers for Disease Control, National Cancer Institute, Commission on Cancer, American Cancer Society, American Joint Commission on Cancer, and the U.S. Department of Health and Human Services. The Cancer in North America database collects cancer information from specific geogr aphic states and provinces. User functionality Users are able to search by a variety of factors. Users are able to search by geography (North America, Canada, United States),cancer site, year range, sex, race/ethnicity, and standard population. Data is then displayed in map and table format. Users have the option of viewing the data in bar chart format as well. On the provided table users are able to click on specific regions for more detailed information including population at risk, total cases, crude rate, and age adjusted rate. Data Notes The year and reference is clearly marked for every data query underneath the data table or map. The most recent data is available from 2007. There is no indication when the site will be updated.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.258 | 0.096 |
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".