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Scientific Business Abstracts
Xin Meng, Taha Elajnaf, Michelle Ma, Fadil Hannan, Benjamin P. Fairfax, Rebecca Evans, Charlotte Carter, Aran Singanayagam, Deepa J. Arachchillage, Andrew S. Cowburn, Katharine M. Lodge, Julian Matthewman, Anna Schultze, Krishnan Bhaskaran, Spiros Denaxas, Kathryn E. Mansfield, Sinéad Langan, Philip M. Bath, Lisa J Woodhouse, Xin Chen, Jonathan M. Garibaldi, Alan Montgomery, Terence J. Quinn, Nikola Sprigg, Xin Chen, Alan Montgomery, Sihao Xiao, Taisha Peplowska, Adam R. Creigh, Lexie Urquhart, Helen Devine, Daniel P. Gale, Detlef Böckenhauer, Maryline Fresquet, E. G. Williams, Conor J. Sugden, Melanie Chan, Katie Wong, Konstantinos Tzoumkas, Lior Horonchick, Roger Clark, Peter Mündel, Uwe Andag, Jeffrey H. Miner, Rachel Lennon, Nicole Endlich, Behzad Najafian, Sumit Aggarwal, Eleanor M. Kerr, Amanah Lewis-Wade, José Antonio Valer, Stefan J. Marciniak, Jessica Sayfullaeva, Yan Chen, Ying Zhai, Elifnur Kılıç, Aoife K. O’Connell, Andrea Kwakowsky, Victoria Simms, Sarah Rowland‐Jones, Anthony Y.Y. Hsieh, Ananda S. Mirchandani, Luke Flower, Emilio G. Vozza, Anthony Ng, Eckart M. D. D. De Bie, Neda Farahi, Zhenguang Zhang, Clare Bryant, Charlotte Summers, Cyril Kocherry, Calum Foreath, Heather J. Merchant, Colin E. Murdoch, Jennifer Kerr, Jennifer E. Gallagher, Alison D. McNeilly, Rory J. McCrimmon, B. D. Palmer, James Geary, Richard J. Stanton, Awen Gallimore, Andrew Godkin, Katie Marwick, Cathy Wyse, Pamela Bowman, Maria Salguero-Bermonth, Eva Wooding, Mary Lee, Maggie Shepherd, James Tonks, Ben Edwards, Lorna Torrens, Tamsin J Ford, Lisa R. Letourneau, Sarah E. Flanagan, Tamsin Newlove‐Delgado, Siri Atma W. Greeley, Andrew T. Hattersley, Viyaasan Mahalingasivam, Dorothea Nitsch, Arvid Sjölander, Anne-Laure Faucon, Kate Mansfield, Juan Jesús Carrero, Laurie A. Tomlinson, Jeffrey Johns, Jui Vyas, Faraz M Ali, John R Ingram, Muaad Eghlileb, Annabelle Xiao Hui Lim, K. S. Clifford Chao, Sam Salek, Andrew Finlay, Peter Taylor, WY Cheung, Danijela Tatović, Hayley Hutchings, Charlotte K. Boughton, Julia Fuchs, John W Gregory, Roman Hovorka, Colin Dayan, Faiqa Mubashar, Sophia Tribelhorn, Jun Yong, Cadhan O'Garra, Raechelle A. D’Sa, Guozheng Wang, Simon T. Abrams, Cheng Hock Toh, Jiya Jacob, Onyebuchi Okosieme, Lisha Jeena, Ruramayi Rukuni, Andrea M. Rehman, Sarah Rowland‐Jones, Celia L. Gregson, Julian Shellard, Hilda Mujuru, Hilda Mabuda, Molly Chisenga, Tsitsi Bandason, Nyasha V Dzavakwa, Lackson Kasonka, Rashida A. Ferrand, Juan Delgado‐SanMartin, Niamh Errington, Narayan Schuetz, Anders Johnson, Varsha Gupta, Steven G. Hershman, Mark Toshner, Martin R. Wilkins, David G. Kiely, Roger Thompson, Euan A. Ashley, Dennis Wang, Allan Lawrie, Susan J. Hillman, Callum G. Brownstein, Thomas Bulea, Karen Suetterlin, Sana Shah, J Catabung, Jill J. Ranger, Claire L. Shovlin, Anya Topiwala, Daniel Levey, Hang Zhou, Joseph D. Deak, Keyrun Adhikari, Klaus P. Ebmeier, Steven Bell, Stephen Burgess, Thomas Nichols, Michael Gaziano, Murray Stein, Joel Gelernter, Dongyang Li, Karina Al-Dahleh
Bibliographic record
VenueQJM · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsInstitute of Infection and Immunity
FundersBritish Heart Foundation
KeywordsComputer 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.411
Threshold uncertainty score0.998
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.029 |
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 head0.036
GPT teacher head0.208
Teacher spread0.173 · 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; both teacher heads agree on what is shown here.
Study designNot applicable
Domainnot available
GenreOther
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
Published2025
Admission routes1
Has abstractno