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Atlas cancer mapping abroad

2022· article· en· W4312472145 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInterCarto InterGIS · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
FundersLomonosov Moscow State UniversityRussian Geographical Society
KeywordsAtlas (anatomy)CancerVariety (cybernetics)DiseaseGeographyCartographyData scienceMedicineComputer sciencePathologyArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.037
GPT teacher head0.319
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it