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Record W4318900136 · doi:10.1155/2007/540469

Post‐Genomic Technologies

2007· article· en· W4318900136 on OpenAlexaff
Donna G. Albertson, D. Goossens, Lien Heyrman, B Harding, Wim Glassee, Peter De Rijk, Jurgen Del‐Favero, Peggy Eis, Timothy A. Graubert, Patrick Cahan, Deepa Deepa, Rebecca R. Selzer, Todd, W. Richmond, Xia Li, McLeod, James M. Cheverud, Timothy J. Ley

Bibliographic record

VenueAnalytical Cellular Pathology · 2007
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsComputational biologyComputer scienceWorld Wide WebData scienceBiology

Abstract

fetched live from OpenAlex

Genomic DNA copy number aberrations are frequent in solid tumors and genomic profiling of a number of different tumor types by array CGH has revealed variety in the numbers and types of copy number alterations in the tumor genomes. Copy number profiles distinguish tumor subtypes, and specific genomic alterations can be associated with other factors such as hormonal status or TP53 mutation status.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0380.042

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

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