Bibliographic record
Abstract
The January 2010 issue of AJT, which begins our 10th volume, will offer some improved features to our readers. First, we are incorporating an expanded version of The AJT Report, which will be evaluated for readership enthusiasm after four of these expanded versions have been published. Second, we are publishing the guidelines for managing the H1N1 influenza problem in organ transplant recipients. We are also launching a new feature, the virtual issues. Each virtual issue will be a collection of AJT articles covering a specific topic to facilitate readership overview of that topic. Our first topic will be AJT publications of interest in infectious diseases. Each virtual issue will have a designated guest editor. The current guest editor, Dr. Atul Humar, has compiled a number of articles that should be of interest to our readership in terms of introducing infectious disease issues in organ transplantation. Over the next year, we anticipate creating virtual issues in Hepatitis C and liver transplantation, other liver transplantation issues, basic science, kidney transplantation and antibody-mediated rejection, as well as others. As the virtual issues are created by an expert collating the seminal contributions in a topic area, we believe that they will be an essential tool that will enable our readership to efficiently identify controversies and progress in the field. In addition, we hope that AJT readers will find the changes to the AJT homepage (http://www.amjtrans.com) useful. The editorial office and members of the editorial board all welcome feedback on these changes, which we hope will enhance the utility of the American Journal of Transplantation project for health care professionals in many fields.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.285 | 0.180 |
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".