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Record W4401098268 · doi:10.1007/s10653-024-02132-x

Verification of correction factors for determining mean annual levels of radon in underground facilities

2024· article· en· W4401098268 on OpenAlexaboutno aff
Lidia Fijałkowska–Lichwa, Jerzy Olszewski

Bibliographic record

VenueEnvironmental Geochemistry and Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Environmental scienceRadonConsistency (knowledge bases)Ventilation (architecture)Work (physics)StatisticsHydrology (agriculture)MeteorologyGeographyEngineeringMathematicsArchaeologyMechanical engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

The first verification of a tool developed to improve the work of controlling bodies, managers and employees of underground facilities subject to radiation protection requirements was conducted. The recommended values of correction factors were verified using archival results of measurements conducted for the Institute of Occupational Medicine in Łódź in seven underground workplaces in Poland over exposure periods of a month (10,8678 data) and a quarter of a year (53,688 data). In a cave two groups of monthly factors, produced estimates with almost 70% to 99% consistency with the measured values. Along tourist routes located in mines, a similar fit was obtained using three groups of correction factors for measurement results from March, June and July. In the extraction areas of active underground mines, the best fit was produced by factors calculated as averages for spaces varying in the degree of insulation and ventilation method, while in other departments of mining plants, by correction factors recommended for facilities equipped with mechanical ventilation systems. All the quarterly correction factors produced the best fit between estimated mean annual concentrations and measurement results obtained in the second quarter of the calendar year. A wide variation in result consistency (from 20-30 to 65-80%) obtained for two underground tourist routes in the fourth quarter of the year demonstrates that it is best not to adopt results from this measurement period (October-December) for estimating mean annual radon concentration using the set of quarterly correction factors.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.385
Teacher spread0.273 · 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.

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
Published2024
Admission routes1
Has abstractyes

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