Bibliographic record
Abstract
MODERATOR: Tony Alves Aries Systems Corporation North Andover, Massachusetts SPEAKERS: Elizabeth Caley Meta, Chan Zuckerberg Initiative Toronto, Ontario, Canada Anita Bandrowski SciCrunch/NIF/RRID University of California, San Diego La Jolla, California Timothy Houle Massachusetts General Hospital and Harvard Medical School Boston, Massachusetts Chadwick DeVoss NEX7, StatReviewer Madison, Wisconsin REPORTER: Darren Early American Society for Nutrition Rockville, Maryland Tony Alves introduced the session by informing the audience he would focus on three new tools: Meta, the Resource Identification Initiative, and StatReviewer. Elizabeth Caley began by noting that Meta had recently been acquired by the Chan Zuckerberg Initiative, which strives to develop collaborations between scientists and engineers, enable tools and technologies, and build support for science. The Meta Science platform was built using artificial intelligence to enable article discovery. It is currently used by researchers at >1200 institutions and includes 44 million unique pages. The Bibliometric Intelligence tool uses deep predictive profiling to predict Eigenfactor, citations, and top percentile rank in order to answer three core questions about a submitted manuscript: 1) Is it a fit for the journal? 2) What is its potential impact? and 3) Who are the best reviewers for it? This analysis helps editors pinpoint manuscripts at the time of submission that are appropriate for their journals and likely to be of high impact. Bibliometric Intelligence can thus be used to pre-rank manuscripts and intelligently cascade them to sister journals within a publisher’s portfolio. The algorithm’s results are regularly tested against the actual performance of articles. A detailed white […]
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.769 | 0.589 |
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.
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.
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".