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Record W4312065570 · doi:10.12703/r-01-0000019

A Genetic roadmap to the DNA damage response to genotoxic agents in human cells

2022· letter· en· W4312065570 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFaculty Reviews · 2022
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDNA damageDNA repairDNABiologyGeneCRISPRCancerComputational biologyCancer cellGenomeHuman genomeCancer researchGeneticsBioinformatics

Abstract

fetched live from OpenAlex

To maintain genome fidelity and prevent diseases such as cancer, our cells must constantly detect, and efficiently and precisely repair, DNA damage. Paradoxically, DNA-damaging agents in the form of radiation and chemotherapy are also used to treat cancer. Olivieri et al. used a CRISPR-based screen to identify genes that, when disrupted, lead to sensitivity or resistance to 27 different DNA-damaging agents used in the lab and/or in the clinic to treat cancer patients [1]. Their results reveal multiple new genes and connections that regulate these critical DNA damage repair pathways, with implications for basic and clinical research as well as cancer therapy.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.049
GPT teacher head0.324
Teacher spread0.275 · 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