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Record W3129379232 · doi:10.1038/s41588-021-00791-5

The NCI Genomic Data Commons

2021· article· pl· W3129379232 on OpenAlexaff
Allison P. Heath, Vincent Ferretti, Stuti Agrawal, Maksim An, James C. Angelakos, Renuka Arya, Rosita Bajari, Bilal Baqar, Justin H. B. Barnowski, Jeffrey Burt, Ann Catton, Brandon F. Chan, Fay Chu, Kim Cullion, Tanja M. Davidsen, Phuong-My Do, Christian Dompierre, Martin L. Ferguson, Michael Fitzsimons, Michael E. Ford, Miyuki Fukuma, Sharon Gaheen, Gajanan Ganji, Tzintzuni I. Garcia, Sameera S. George, Daniela S. Gerhard, Francois Gerthoffert, Fauzi Gomez, Kang Han, Kyle M. Hernandez, Biju Issac, Richard F. W. Jackson, Mark A. Jensen, Sid Joshi, Ajinkya Kadam, Aishmit Khurana, Kyle M. J. Kim, Victoria E. Kraft, Shenglai Li, Tara M. Lichtenberg, Janice Lodato, Laxmi Lolla, Plamen Martinov, Jeffrey A. Mazzone, Daniel P. Miller, Ian Miller, Joshua S. Miller, Koji Miyauchi, Mark W. Murphy, Thomas Nullet, Rowland O. Ogwara, Francisco Ortuño, Phuong Lan Pham, Maxim Y. Popov, James J. Porter, Ray Powell, Karl Rademacher, Colin P. Reid, Samantha Rich, Bessie Rogel, Himanso Sahni, Jeremiah Savage, Kyle A. Schmitt, Trevar Simmons, Joseph Sislow, Jonathan Spring, Lincoln Stein, S. Mažeika P. Sullivan, Yajing Tang, Mathangi Thiagarajan, Heather D. Troyer, Chang Wang, Zhining Wang, Bedford L. West, Alex Wilmer, Shane Wilson, Kaman Wu, William P. Wysocki, Linda Xiang, Joseph T. Yamada, Liming Yang, Christine Yu, Christina K. Yung, Jean C. Zenklusen, Junjun Zhang, Zhenyu Zhang, Yuanheng Zhao, Ariz Zubair, Louis M. Staudt, Robert L. Grossman

Bibliographic record

VenueNature Genetics · 2021
Typearticle
Languagepl
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsTerahertz Technology Solutions (Canada)Centre Hospitalier Universitaire Sainte-JustineOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsBiologyPetabyteGenomicsCommonsInterface (matter)CancerComputational biologyGeneticsBig dataGenomeData miningGeneComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.012
metaresearch head score (Gemma)0.097
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: Empirical · Consensus signal: none
Teacher disagreement score0.291
Threshold uncertainty score0.974

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.097
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.014
Science and technology studies0.0020.003
Scholarly communication0.0120.009
Open science0.0070.013
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.2910.256

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 head0.014
GPT teacher head0.275
Teacher spread0.262 · 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
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

Citations211
Published2021
Admission routes1
Has abstractno

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