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Record W4386817860 · doi:10.1093/rpd/ncad152

Electron paramagnetic resonance spectroscopy for the detection of radiation exposure in dreissenid mussels

2023· article· en· W4386817860 on OpenAlexafffund
Margarita Tzivaki, Amna Hassan, Edward Waller

Bibliographic record

VenueRadiation Protection Dosimetry · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRadiation Effects and Dosimetry
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity Network of Excellence in Nuclear Engineering
KeywordsElectron paramagnetic resonanceSpectroscopyRadiationDosimetryEnvironmental scienceSIGNAL (programming language)Materials scienceSampling (signal processing)Nuclear magnetic resonanceAnalytical Chemistry (journal)PhysicsChemistryNuclear medicineComputer scienceOpticsEnvironmental chemistryMedicine

Abstract

fetched live from OpenAlex

Electron paramagnetic resonance (EPR) spectroscopy, established for radiation measurements in calcified tissues, was identified as a methodology that merits investigation for the purpose of environmental radiation measurements using dreissenid mussels from the Great Lakes. With the refinement of sample preparation and measurement protocols, a linear relationship of dose with the peak-to-peak height of the radiation-induced signal at g = 2.0034 was established. A dedicated analysis algorithm was developed to process batches of samples, eliminating the need for manual peak-to-peak height measurement. Varying background EPR signals were identified in different sampling groups, with samples gathered in winter having a markedly lower background signal. Through optimisation of spectrum acquisition normalisation methods, it was possible to resolve doses as low as 0.2 Gy. This work provides further validation that EPR dosimetry of shelled species has the potential to contribute to better characterisation of absorbed doses in aquatic environments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.011
GPT teacher head0.238
Teacher spread0.226 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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