Bibliographic record
Abstract
To assess the state and prospects for the development of cancer mapping in Russia, country and world experience in the preparation of cartographic works on cancer topics is useful. For this purpose, an attempt was made to trace the development of cancer mapping on the example of foreign atlas works and some publications on their review. National (Australian Cancer Atlas, Taiwan cancer map, Canadian Cancer Incidence Atlas, etc.) and world (The Cancer Atlas, Global burden of cancer women, Global Atlas of Palliative Care at the End of Life, etc.) atlases are presented. They deserve attention in terms of promoting the content and methodological side of cancer mapping, as well as expanding their functionality. The review of atlases reflects the diversity of approaches to their development, the differences in the indicators used, and the prevailing trends in the presentation of results for solving the problems facing medicine and society. The concept of “burden” is the basis of the concept of a significant part of the atlases. It covers various aspects of the manifestation of cancer (from medical to socio-economic) and is displayed in atlases, depending on their purpose, with varying degrees of completeness. From studying the spatio-temporal spread of cancer, atlas studies are moving on to building and testing hypotheses about the factors and determinants of cancer on the basis of a variety of synergies of natural, social, economic, environmental, behavioral and other features.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.019 |
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".