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Record W1744813163 · doi:10.48550/arxiv.1211.2458

Survey of Extra-Low Frequency and Very-Low Frequency Magnetic Fields in Cell Culture Incubators

2012· preprint· en· W1744813163 on OpenAlexaff
Dong Su, Paul Héroux

Bibliographic record

VenuearXiv (Cornell University) · 2012
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsMcGill University
Fundersnot available
KeywordsLow frequencyPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A typical cell culture CO2 incubator was probed in detail to document the pattern of 60-Hz magnetic fields (MFs) inside the unit, as well as the ability of the incubator to attenuate environmental MFs. Subsequently, a survey of 46 cell culture incubators was performed. The survey measured MFs outside and inside the incubators, the frequency spectrum between 5 and 2000 Hz, and variations over time of the 60-Hz MF. Our measurements show an uneven spatial distribution, reflecting electronic and electrical components hidden within the walls. Attenuation of environmental MFs varied between 18 % and 33 %, signalling easy penetration into the units. MF levels, frequency spectra and variations over time were very different from one unit to the next. All 46 incubators surveyed had an average field greater than 0.2 microT; among them, 39 (85 %) had an average field greater than 1 microT. There is substantial work to be done in improving control over the MF environment of in vitro experiments in bio-medicine, particularly if they involve cancer cells.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.175
Teacher spread0.157 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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