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Record W4400790310 · doi:10.1667/rade-24-00035.1

EGO to ECO: Tracing the History of Radioecology from the 1950s to the Present Day

2024· review· en· W4400790310 on OpenAlexaff
Bruno F. E. Matarèse, Rhea Desai, Deborah Oughton, Carmel Mothersill

Bibliographic record

VenueRadiation Research · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRadioecologyId, ego and super-egoTracingPsychologyEcologyEnvironmental scienceBiologyPsychoanalysisComputer scienceNuclear physicsPhysics

Abstract

fetched live from OpenAlex

This paper starts with a brief history of the birth of the field of radioecology during the Cold War with a focus on US activity. We review the establishment of the international system for radiation protection and the science underlying the guidelines. We then discuss the famous ICRP 60 statement that if "Man" is protected, so is everything else and show how this led to a focus in radioecology on pathways to "Man" rather than concern about impacts on environments or ecosystems. We then review the contributions of Radiation Research Society members and papers published in Radiation Research which contributed to the knowledge base about effects on non-human species. These fed into international databases and computer-based tools such as ERICA and ResRad Biota to guide regulators. We then examine the origins of the concern that ICRP 60 is not sufficient to protect ecosystems and discuss the establishment of ICRP Committee 5 and its recommendations to establish reference animals and plants. The review finishes with current concerns that reference animals and plants (RAPs) are not sufficient to protect ecosystems, given the complexity of interacting factors such as the climate emergency and discusses the efforts of ICRP, the International Union of Radioecologists and other bodies to capture the concepts of ecosystem services and ecosystem complexity modelling in radioecology.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.722
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.104
GPT teacher head0.397
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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