Bibliographic record
Abstract
Antibodies to many Epstein-Barr virus (EBV) gene products are present in sera from infected human carriers. Historically, the ready demonstration of antibodies to lytic cycle proteins in particular allowed the seroepidemiological studies, which implicated EBV as a factor in various diseases (1–4). However, human sera also contain antibodies to EBV latent gene products, as first demonstrated in an immunofluorescence test that detected predominantly EBNA1 (5). Subsequently, human sera proved to be remarkably useful reagents for detecting and characterizing various latent EBV proteins in Western blot assays (e.g., refs. 6–8). Indeed, even now that monoclonal antibodies (MAbs) are available to many of the EBV latent proteins, human sera continue to be useful for “EBNotyping” assays in which EBV isolates can be distinguished by virtue of the characteristic fingerprint of variable-sized EBNA proteins (e.g., refs. 9–12). An example of EBNotyping is shown in Fig. 1.
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 | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".