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Record W3120363063 · doi:10.18280/ijsse.100605

Numerical Modeling of the Exposure on Radiofrequency Radiation of Marine Mammal Observers During Their Shift: A Case Study

2020· article· en· W3120363063 on OpenAlexvenueno aff
Christos Spandonidis, Kyriakoula Arvaniti

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCrewOccupational exposureRisk assessmentEnvironmental scienceTransmission (telecommunications)Computer scienceUltra high frequencyRisk analysis (engineering)Marine engineeringReliability engineeringEnvironmental healthTelecommunicationsEngineeringAeronauticsComputer securityMedicine

Abstract

fetched live from OpenAlex

Following the standard numerical modeling approach for Electromagnetic Field (EMF) radiation exposure prediction, we intend to provide an analytical framework to Marine Mammal Observers (MMOs) for dynamic risk assessment; enhancing thus occupational health and safety awareness. The analysis is based on power levels and antenna characteristics reported by MMOs for two systems (VHF and UHF) located close to the working environment. Whilst occupational exposure limits apply for MMOs, as for the rest crew (seismic and maritime), evaluation of exposure levels against general public limits is presented as well. At present we have restricted our study to single-source radiation, as well as we did not consider any irregularities due to system malfunction. The worst-case scenario of continuous RF transmission was considered. Risk assessment indicated regions where radiation exposure is higher than the permissible limits. Uncertainty due to the operational environment is inserted in methodology using an uncertainty coefficient. A list of control measures is proposed, to support both MMO’s and Operators’ decision making.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.033
GPT teacher head0.280
Teacher spread0.246 · 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 designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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