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Record W7009413607

Electromagnetic pollution in the urban environment and risk or cancer

2024· other· en· W7009413607 on OpenAlexaboutno aff

Bibliographic record

VenueRepositorio Institucional de la Universidad de Málaga (University of Málaga) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInternational agencyCancerHuman healthUrban environmentAgency (philosophy)Mechanism (biology)Environmental pollutionProduction (economics)Cancer incidenceElectromagnetic field
DOInot available

Abstract

fetched live from OpenAlex

Social, national and international interest in the health risks of exposure to electromagnetic fields in the urban environment is growing in our increasingly technological Society. In Spain, very few measurements have been made in cities, while in other countries such as Canada, exposure levels have been evaluated in more than 60 cities. The International Agency for Research on Cancer (IARC) has classified low-frequency magnetic fields as “possibly carcinogenic” in humans. It is important that Information Systems include data about urban exposure levels and their relationship with cancer incidence in order to minimize risks. To date, it is still not clear what is the molecular mechanism of interaction between magnetic field and biological tissue. There is evidence that magnetic fields could increase the production of reactive oxygen species, induce damage to the DNA molecule and alter signaling pathways that lead to carcinogenesis; although it is still not sufficiently demonstrated.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0180.003

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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