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Record W4319294613 · doi:10.1667/rade-22-00193.1

The REPAIR Project, a Deep-Underground Radiobiology Experiment Investigating the Biological Effects of Natural Background Radiation: The First 6 Years

2023· article· en· W4319294613 on OpenAlexaff
Jake Pirkkanen, Christine Lalonde, Michel Lapointe, Taylor Laframboise, Marc S. Mendonca, Douglas R. Boreham, Sujeenthar Tharmalingam, Christopher Thome

Bibliographic record

VenueRadiation Research · 2023
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsRadiobiologyScope (computer science)Natural (archaeology)Medical physicsComputer sciencePhysicsRadiation therapyArchaeologyMedicineHistory

Abstract

fetched live from OpenAlex

In 2017, a special edition of Radiation Research was published [Oct; Vol. 188 4.2 (https://bioone.org/journals/radiation-research/volume-188/issue-4.2)] which focused on a recently established radiobiology project within SNOLAB, a unique deep-underground research facility. This special edition included original articles, reviews and commentaries relevant to the research goals of this new project which was titled Researching the Effects of the Presence and Absence of Ionizing Radiation (REPAIR). These research goals were founded in understanding the biological effects of terrestrial and cosmic natural background radiation (NBR). Since 2017, REPAIR has evolved into a sub-NBR radiobiology research program which investigates these effects using multiple model systems and various biological endpoints. This paper summarizes the evolution of the REPAIR project over the first 6-years including its experimental scope and capabilities as well as research accomplishments.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.081
GPT teacher head0.408
Teacher spread0.326 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

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