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Record W4393810354 · doi:10.5281/zenodo.3662712

Human papillomavirus integration transforms chromatin to drive oncogenesis: data and results

2023· dataset· en· W4393810354 on OpenAlexaff
Mehran Kaimzadeh, Christopher Arlidge, Ariana Rostami, Mathieu Lupien, Scott V. Bratman, Michael M. Hoffman

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsChromatinHuman papillomavirusCarcinogenesisComputer scienceBiologyComputational biologyGeneticsCancerMedicineInternal medicineGene

Abstract

fetched live from OpenAlex

Datasets and results accompanying the manuscript: Human papillomavirus integration transforms chromatin to drive oncogenesis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.048
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0480.046

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 head0.083
GPT teacher head0.328
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicGenetic factors in colorectal cancer→French-language works237,207→