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Nonionizing Radiation: Extremely Low Frequency

2021· other· en· W3130988966 on OpenAlexaff
Mona Shum, Jesse Cooper

Bibliographic record

VenuePatty's Industrial Hygiene · 2021
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectromagnetic Fields and Biological Effects
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsElectric fieldMagnetic fieldExtremely low frequencyRange (aeronautics)Nuclear magnetic resonanceOccupational exposurePhysicsElectromagnetic fieldElectrical engineeringAcousticsMaterials scienceEngineeringMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract The extremely low‐frequency (ELF) range generally consists of frequencies from greater than zero to 2 kHz. Electric fields are produced by charges, whereas magnetic fields are produced by charges in motion. Several organizations publish occupational and public exposure guidelines for electric and magnetic fields that are protective against acute effects and typical daily exposures are usually well below these levels. Medical device wearers can experience interference from electric and magnetic fields and wearers must be aware of the limitations near particular equipment or devices. There is limited evidence for any effects associated with chronic exposure to electric and magnetic fields. For measurement of electric and magnetic fields in the ELF range, these two fields must be measured independently. A number of different meters are available to measure electric and magnetic fields, the choice of which depends on what field (electric vs. magnetic) and frequency range is being measured, the anticipated field level, and the sensitivity desired. Controls for reducing electric and magnetic fields include engineering and design controls, increasing distance from the source, and for electric fields, certain personal protective equipment meant to insulate from contact with charged metallic surfaces.

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 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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.429
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.249
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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