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Record W4403756045 · doi:10.1002/9781394332175.ch4

Radioactive Contamination of Food and Trophic Transfer

2024· other· en· W4403756045 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
Fundersnot available
KeywordsTrophic levelContaminationRadioactive contaminationEnvironmental scienceEnvironmental chemistryChemistryBiologyEcology

Abstract

fetched live from OpenAlex

Human foodstuffs can be naturally or artificially contaminated with radionuclides. This radioactive contamination can be transferred to humans when they eat. A large proportion of human contamination by radionuclides, both natural and artificial, comes from the diet. Some of the radionuclides in food are of natural origin and belong to the uranium and thorium families. The transfer factors from atmosphere to fruit will vary according to radionuclide speciation in the physical medium. Information on the transfer of radionuclides to fruit was almost absent in the former TRS 364 “Manual of parameter values for the prediction of radionuclide transfer in temperate environments”. The subarctic food chain of lichen–reindeer–human in Finland has been extensively studied for cesium and strontium radionuclides, but little for transuranics. Yet, radionuclide enrichment can lead to exceptionally high body loads in indigenous Sami and Inuit populations consuming large quantities of reindeer meat and edible organs.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.417
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0160.000

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 head0.007
GPT teacher head0.205
Teacher spread0.199 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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