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Record W4281674430 · doi:10.1109/mspec.2022.9792155

Contributors

2022· article· en· W4281674430 on OpenAlexaff
Jean Kumagai, Elizabeth Bretz, Mark Montgomery, Glenn Zorpette, Evan Ackerman, Samuel G. Moore, Philip Ross, David Schneider, Eliza Strickland, Randi Klett, Erik Vrielink, Willie D. Jones, Michael Koziol, Joseph Levine, Alan Gardner, Ramona Foster, Robert Charette, Steven Cherry, Charles Q. Choi, Peter Fairley, Edd Gent, W Gibbs, Mark Harris, Allison Marsh, Prachi Patel, Julianne Pepitone, Lawrence Ulrich, Emily Waltz, Kathy Pretz, Joanna Goodrich, Peter Tuohy, Multimedia Production Specialist, Michael Spector, Gail Schnitzer, Felicia Spagnoli, Nicole Evans Gyimah, Susan Hassler, Ella Atkins, Francis Doyle, Matthew Eisler, Shahin Farshchi, Alissa Fitzgerald, Jonathan M. Garibaldi, Benjamin Groß, Lawrence Hall, Jason Hui, Leah H. Jamieson, Mary Jepsen, Michel M. Maharbiz, Somdeb Majumdar, Lisa May, Carmen S. Menoni, Ramune Nagisetty, Paul Nielsen, Sofia Olhede, Christopher Stiller, Wen Tong, Qusi Alqarqaz, Stamatis Dragoumanos, Madeleine Glick, Francesca Iacopi, Cecilia Metra, Mirela Sechi, Annoni Notare, Shashi Pandey, John Purvis, Steven Heffner, Mark David, Erik Albin, Kui Liu, Saifur Elect, Mary Rahman, John M. Randall, Susan Walz, Stephen Kathy, Lawrence S. Phillips, David Koehler, Bruno Meyer, James Matthews, Deborah Cooper, Franco Maloberti, Ruth Dyer, Ben Khaled, Manfred Letaief, Fred, Ian Schindler, Paul Cunningham, Claudio Cañizares, Christina Schober, Ali H. Sayed, Dalma Novak, Greg Gdowski, Barry Tilton, Theresa Brunasso, Johnson Asumadu, Bob Becnel, Timothy Lee, Robert Anderson, Antonio Luque, Theodore Hissey

Bibliographic record

VenueIEEE Spectrum · 2022
Typearticle
Languageen
Field
Topic
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Lorem Ipsum

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.349
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.6510.564

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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; the direct Gemma label and the distilled Codex classifier 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueIEEE SpectrumFrench-language works237,207