Bibliographic record
Abstract
Abstract There are two approaches to scientific investigation, the common approach (proving one’s theory) and Popper’s approach (falsification of one’s theories). Popper’s approach has advantages as well as dangers (being perceived as not sure of one’s theories, or even be hostile to them—an ‘auto‐traitor’). Nevertheless, the Popper approach can bridge the gap between inhibition (directly observable) and inhibitory regulation (not directly observable). Suppression of immune responses by antigen‐specific antibody has led to theories regarding immunoregulation by immune products. There are many immune products capable of regulating immune responses. The inhibitory outcomes of this regulation have been called coinhibition and immune checkpoint inhibition. Coinhibition should be used when regulation begins at the cell surface or in the cell cytoplasm, which opens up the possibility of antigen‐specific regulation. Immune checkpoint inhibition should be used when the initiating inhibitory event occurs in the nucleus, such as by directly affecting the cell cycle, where the concept of antigen‐specific regulation is more difficult to invoke. These forms of immunoregulation could be corrupted by viral infections, such as in COVID‐19 infections.
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 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.014 |
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".