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Record W4244390072 · doi:10.1504/ijlr.2020.113535

Living in radioactive environments: a non-human perspective

2020· article· en· W4244390072 on OpenAlexaff
Carmel Mothersill, Colin Seymour

Bibliographic record

VenueInternational Journal of Low Radiation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAnthropocentrismCommissionRisk analysis (engineering)Environmental resource managementEnvironmental planningEcosystemPerspective (graphical)Natural (archaeology)Environmental ethicsComputer scienceBusinessEcologyEnvironmental sciencePolitical scienceBiologyLawArtificial intelligence

Abstract

fetched live from OpenAlex

Radiation protection of non-humans has recently been integrated into the ICRP (International Commission on Radiation Protection) framework using a reference animal and plant approach matching the anthropocentric 'reference human' approach. While this is simple to implement it has many drawbacks and is essentially focusing on measurements of uptake and transfer of radionuclides in individuals. This ignores the complexity and interdependence of natural ecosystems. It also ignores the biology involved in management of radiation damage in wild populations. To address these concerns international efforts are being made to develop a more ecocentric or holistic approach. Especially important is the recognition that ecosystems are very complex and that adverse emergent properties of these systems such as biodiversity collapse are not predictable from measurement of impacts on individuals. The paper discusses the limitations of current ICRP approaches and considers some promising new ideas, which may lead to more integrated protection systems involving the ecosystem as a central focus rather than the individual.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.262
Teacher spread0.255 · 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
Published2020
Admission routes1
Has abstractyes

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