Bibliographic record
Abstract
It always begins the same way.First a sniffle.Then a runny nose.Maybe bleary eyes and a headache.But within a week you feel better and get on with your life.The sniffles were not what we had in mind.This issue promises to be one of our most hard-hitting to date as we tackle this year's first theme: infectious diseases.Join Joseph Catapano as he examines West Nile Virus, an endemic and emerging disease in North America.Elena Igwe delivers a potent response to antibiotic resistance with new developments in anti-microbial peptides.As of late, Avian Bird Flu has been all over the news.Explore influenza H5N1's potential for pandemic in my article and learn about probable treatments with Romy Cho's report on influenza vaccines and antiviral medications.Finally, in a collaborative article by Niranjan Vijayakanthan, Mohammad Zubairi, Hamilton Candundo, Brent Mollon, Gregory Agate, they discuss the possibility of the vanquished ing as a looming biological weapon.
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 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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; both teacher heads agree on what is shown here.
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".