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Record W2010833279

THE BIOLOGICAL EFFECTS OF IONIZING RADIATION: EPIDEMIOLOGICAL SURVEYS AND LABORATORY ANIMAL EXPERIMENTS. IMPLICATIONS FOR RISK EVALUATION AND DECISION PROCESSES

2010· article· en· W2010833279 on OpenAlexaboutno aff
J.I. Fabrikant

Bibliographic record

VenueeScholarship (California Digital Library) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsNational laboratoryLibrary scienceBiological sciencesAgency (philosophy)Political scienceMedicinePhysicsEngineering physicsSociologyBiologyComputer scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

served faithfully and effectively to discuss, to review, to evaluate and to report on three important matters of societal concern: (1)) to place into perspective the actual and potential harm to the health of man and his decendants to be expected in the present and in the future from those societal activities involving the use of ionizing radia tions; (2) to develop quantitative indices of harm based on doseresponse relationships to provide a scientific basis for the evaluation of somatic and genetic risk and protection of human populations exposed to low-level radiation; and (3) to identify the sources and levels of radiation which could cause harm, to assess their relative importance, and to provide a framework on how to reduce unnecessary radiation exposure to human populations.To a greater or lesser extent, each advisory committee on radiation-such as the UNSCEAR.2 the ICRP.3 the NCRP.4 the NRPB,5 and others in France, Canada, and elsewhere in Europe and Japan, and the BEIR Committee-have dealt with these matters.But significant differ ences occur in the scientific reports of these various bodies, and we should expect differences to occur, because of the charge, the scope, and the composition of each committee, and probably most important, because of public attitudes existing at the

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)0.005
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.376
Teacher spread0.300 · 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
Published2010
Admission routes1
Has abstractyes

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