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Record W2411193573 · doi:10.6084/m9.figshare.44243.v1

Upper, lower, and point estimates of AF, based on geometric mean exposure of ELF

2011· article· en· W2411193573 on OpenAlexaboutno aff
Kheifets Leeka, Riti Shimkhada

Bibliographic record

VenueFigshare · 2011
Typearticle
Languageen
FieldMedicine
TopicOcular and Laser Science Research
Canadian institutionsnot available
Fundersnot available
KeywordsMathematicsPoint (geometry)Geometric meanStatisticsGeometry

Abstract

fetched live from OpenAlex

<b>Copyright information:</b>Taken from "Public Health Impact of Extremely Low-Frequency Electromagnetic Fields"Environmental Health Perspectives 2006;114(10):1532-1537.Published online 22 Jun 2006PMCID:PMC1626420.This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI Numbers in parentheses indicate multiple studies for one country. Figure is based on exposure distributions for specific countries and estimate of effect from pooled analysis by : Belgium: ; Germany: ; ; Canada: ; United States (1):; United States (2): . Vertical bars indicate upper and lower AF estimates.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0580.000

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 head0.066
GPT teacher head0.301
Teacher spread0.235 · 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 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
Published2011
Admission routes1
Has abstractyes

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