Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interferon (IFN)-g is a cytokine produced mostly by activated T cells and NK cells that has complex effects on immune and nonimmune cells. IFN-g plays important roles in inflammation, usually in synergy with other cytokines, such as IL-1b and TNF-a. The uniqueness of IFN-g lies in its ability to induce major histocompatibility complex (MHC) expression in many tissues, making it particularly relevant to transplantation. The results of graft rejection in the absence of IFN-g show that IFN-g modulates but is not essential for the allogeneic responses, suppressing generation of CTL. In vivo IFN-g has a protective role early in the response to vascularized organ allografts: transplants in mice have a tendency to develop necrosis when IFN-g is not available, apparently by failure of the microcirculation. The lack of IFN-g greatly reduces the induction of MHC in organ allografts, and it is possible that this is indirectly related to the protective effect of IFN-g. Nevertheless IFN-g also promotes graft vessel disease later in the course of the transplant. Thus IFN-g has diverse and potentially contradictory effects on organ allograft survival, acting both on the immune system and on the graft itself, the net effect depending on the graft type and the time post-transplant.
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.
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) | 0.001 | 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) | 0.002 | 0.001 |
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 it