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Characterization of Large Structural Genetic Mosaicism in Human Autosomes
Mitchell J. Machiela, Weiyin Zhou, Joshua N. Sampson, Michael Dean, Kevin B. Jacobs, Amanda Black, Louise A. Brinton, I‐Shou Chang, Chu Chen, Constance Chen, Kexin Chen, Linda S. Cook, Marta Crous‐Bou, Immaculata De Vivo, Jennifer A. Doherty, Christine M. Friedenreich, Mia M. Gaudet, Christopher A. Haiman, Susan E. Hankinson, Patricia Hartge, Brian E. Henderson, Yun‐Chul Hong, H. Dean Hosgood, Chao A. Hsiung, Wei Hu, David J. Hunter, Lea Jessop, Hee Nam Kim, Yeul Hong Kim, Young Tae Kim, Robert J. Klein, Peter Kraft, Qing Lan, Dongxin Lin, Jianjun Liu, L. Le Marchand, Xiaolin Liang, Jolanta Lissowska, Lingeng Lu, Anthony M. Magliocco, Keitaro Matsuo, Sara H. Olson, Irene Orlow, Jae Yong Park, Loreall Pooler, Jennifer Prescott, Radhai Rastogi, Harvey A. Risch, Fredrick R. Schumacher, Adeline Seow, Veronica Wendy Setiawan, Hongbing Shen, Xin Sheng, Min‐Ho Shin, Xiao‐Ou Shu, David VanDen Berg, Jiu-Cun Wang, Nicolas Wentzensen, Maria Pik Wong, Chen Wu, Tangchun Wu, Yi‐Long Wu, Lucy Xia, Hannah Yang, Pan‐Chyr Yang, Wei Zheng, Baosen Zhou, Christian C. Abnet, Demetrius Albanes, Melinda C. Aldrich, Christopher I. Amos, Laufey T. Ámundadóttir, S.I. Berndt, William J. Blot, Cathryn H. Bock, Paige M. Bracci, Laurie Burdett, Julie E. Buring, Mary Ann Butler, Tania Carreón, Nilanjan Chatterjee, Charles C. Chung, Michael B. Cook, Michael Cullen, Faith G. Davis, Ti Ding, Eric J. Duell, Caroline G. Epstein, Jin‐Hu Fan, Jonine D. Figueroa, J F Fraumeni, Neal D. Freedman, Charles S. Fuchs, Yu-Tang Gao, Susan M. Gapstur, Ana Patiño‐García, Montserrat García‐Closas, J. Michael Gaziano, Graham G. Giles, Elizabeth M. Gillanders, Edward L. Giovannucci, Lynn R. Goldin, Alisa M. Goldstein, Mark H. Greene, Göran Hallmans, Curtis C. Harris, Roger Henriksson, Elizabeth A. Holly, Robert N. Hoover, Nan Hu, Amy Hutchinson, Mazda Jenab, Christoffer Johansen, Kay‐Tee Khaw, Woon‐Puay Koh, Laurence N. Kolonel, Charles Kooperberg, Vittorio Krogh, Robert C. Kurtz, Andrea Z. LaCroix, Annelie Landgren, Maria Teresa Landi, Donghui Li, Linda M. Liao, Núria Malats, Katherine A. McGlynn, Lorna H. McNeill, Robert R. McWilliams, Beatrice S. Melin, Beata Pepłońska, Ulrike Peters, Gloria M. Petersen, Ludmila Prokunina‐Olsson, Mark P. Purdue, You‐Lin Qiao, Kari G. Rabe, Preetha Rajaraman, Francisco X. Real, Elio Ríboli, Benjamín Rodríguez‐Santiago, Nathaniel Rothman, Avima M. Ruder, Sharon A. Savage, Ann G. Schwartz, Kendra L. Schwartz, Howard D. Sesso, Gianluca Severi, Debra T. Silverman, Margaret R. Spitz, Victoria L. Stevens, Rachael Z. Stolzenberg‐Solomon, Daniel O. Stram, Ze-Zhong Tang, Philip R. Taylor, Lauren R. Teras, Geoffrey S. Tobias, Kala Viswanathan, Sholom Wacholder, Zhaoming Wang, Stephanie J. Weinstein, William Wheeler, Emily White, John K. Wiencke, Brian M. Wolpin, Xifeng Wu, Jay S. Wunder, Kai Yu, Krista A. Zanetti, Anne Zeleniuch‐Jacquotte, Regina G. Ziegler, Mariza de Andrade, Kathleen C. Barnes, Terri H. Beaty, Laura J. Bierut, Karl C. Desch, Kimberly F. Doheny, Bjarke Feenstra, David Ginsburg, John A. Heit, Jae H. Kang, Cecilia A. Laurie, Jun Z. Li, William L. Lowe, Mary L. Marazita, Mads Melbye, Daniel B. Mirel, Jeffrey C. Murray, Sarah C. Nelson, Louis R. Pasquale, Kenneth Rice, Janey L. Wiggs, Anastasia L. Wise, Margaret A. Tucker, Luis A. Pérez‐Jurado, Cathy C. Laurie, Neil E. Caporaso, Meredith Yeager, Stephen J. Chanock
Bibliographic record
VenueThe American Journal of Human Genetics · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersNational Institute of Dental and Craniofacial ResearchNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute for Health and Care ResearchNational Eye InstituteWorld Health Organization
KeywordsGenome-wide association studyAutosomeBiologyGeneticsGenetic associationPopulationGeneSingle-nucleotide polymorphismGenotypeChromosomeMedicine
Abstract
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 imitationNot 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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.002
Threshold uncertainty score0.004
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 head0.020
GPT teacher head0.313
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations125
Published2015
Admission routes1
Has abstractno