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The Collaborative Cross, a community resource for the genetic analysis of complex traits
Kenneth Paigen, Gary A. Churchill, Molly A. Bogue, Beverly Paigen, Huei-Ju Pan, Karen L. Svenson, David Airey, Hooman Allayee, Joe M. Angel, Alan Attie, J. Thomas Beatty, William D. Beavis, John K. Belknap, Thomas E. Johnson, Beth Bennett, Wade H. Berrettini, André Bleich, Karl W. Broman, Kari J. Buck, Robert Hitzemann, Edward S. Buckler, Margit Burmeister, Jeremy L. Peirce, Robert W. Williams, Jing Gu, Elissa J. Chesler, Lu Lu, James M. Cheverud, Steven J. Clapcote, Guy Mittleman, Doug Matthews, Melloni N. Cook, Roger Cox, John C. Crabbe, Wim E. Crusio, Ariel Darvasi, Christian F. Deschepper, Bastien Llamas, R. W. Doerge, Charles R. Farber, Juan F. Medrano, Jiřı́ Forejt, Daniel P. Gaile, Steven J. Garlow, Hartmut Geiger, Howard K. Gershenfeld, Terry Gordon, Weikuan Gu, Gerald de Haan, Richard S. Nowakowski, Nancy L. Hayes, Craig Heller, Heinz Himmelbauer, Kent W. Hunter, Hui-Chen Hsu, Fuad A. Iraqi, Boris Ivandic, Howard J. Jacob, Ritsert C. Jansen, Karl J. Jepsen, Dabney K. Johnson, Gerd Kempermann, Christina Kendziorski, Malak Kotb, R. Frank Kooy, Frank Lammert, Jean‐Michel Lassalle, Pedro R. Löwenstein, Aldons J. Lusis, Kenneth F. Manly, Ralph Marcucio, Darla R. Miller, Beverly A. Mock, Jeffrey S. Mogil, Xavier Montagutelli, Grant Morahan, D.G. Morris, Richard Mott, William Valdar, Joseph H. Nadeau, Hiroki Nagase, Bruce F. O’Hara, А. В. Осадчук, Nengjun Yi, Grier P. Page, Abraham A. Palmer, Daniel Pomp, Michal Pravenec, Daniel R. Prows, Zhonghua Qi, Roger H. Reeves, John Roder, Glenn D. Rosen, Eric E. Schadt, Leonard C. Schalkwyk, Ze’ev Seltzer, Kazuhiro Shimomura, Siming Shou, Mikko J. Sillanpää, Linda D. Siracusa, Hans-Willem Snoeck, Jimmy L. Spearow, Lisa M. Tarantino, Tim Wiltshire, David W. Threadgill, Fernando Pardo‐Manuel de Villena, Linda A Toth, Craig H. Warden, Steve Whatley, Dabao Zhang, Min Zhang
Bibliographic record
VenueNature Genetics · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsToronto Centre for PhenogenomicsUniversity of TorontoMcGill UniversityMontreal Clinical Research InstituteMount Sinai Hospital
FundersOak Ridge National LaboratoryNational Heart, Lung, and Blood InstituteMedical Research CouncilUniversity of North Carolina at Chapel HillHealth Science Center, University of TennesseeUniversity of California, Los AngelesSchool of Medicine, Emory UniversitySchool of Medicine, New York UniversityNational Institutes of HealthInternational Livestock Research InstituteNational Institute on Alcohol Abuse and AlcoholismAkademie Věd České RepublikyRijksuniversiteit GroningenNational Cancer InstituteUniversität HeidelbergVanderbilt University Medical CenterCedars-Sinai Medical CenterPurdue UniversityWellcome TrustUniversity of CincinnatiDavid Geffen School of Medicine, University of California, Los AngelesMedical Center, University of RochesterCase Western Reserve UniversityEmory UniversityNorthwestern UniversityUniversity of RochesterJohns Hopkins UniversityAdvanced Scientific Computing ResearchUniversity of Wisconsin-MadisonCincinnati Children's Hospital Medical CenterUniversity at BuffaloHelsingin YliopistoCollege of Medicine, University of CincinnatiShriners Hospitals for ChildrenMcGill UniversityUniversity of PennsylvaniaHebrew University of JerusalemUniversity of TorontoYale UniversityVanderbilt UniversityUniversity of MemphisThomas Jefferson University
KeywordsBiologyTraitComplex diseaseResource (disambiguation)Computational biologyGenetic resourcesQuantitative trait locusGeneGeneticsData scienceBiotechnologyComputer science
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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.013 |
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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 designBench or experimental
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
Citations1,193
Published2004
Admission routes2
Has abstractno