Bibliographic record
Abstract
Experimental Disease: Featured speakers will comment on mouse models of human breast cancer, microbial provocation and susceptibility to gastrointestinal inflammation, host–bacterial interactions in inflammatory bowel disease, and characterization of attaching and effacing E. coli in pigs. Natural Disease: Featured speakers will comment on Beluga whales from the local St Lawrence estuary, bovine neonatal pancytopenia, and canine respiratory disease complex. Industrial and Toxicological Pathology: Featured speakers will comment on Alzheimer disease and novel biomarkers of skeletal muscle, reproductive pathology in nonclinical safety assessment, and genomic and mechanistic insights in druginduced vascular injury in rats. Posters will be on display throughout the meeting. Poster presenters may be available by their presentation during the refreshment breaks corresponding with the platform sessions of the same topic. This exchange is always well received because it is a dedicated time to meet the folks behind our science and to talk pathology with colleagues. It has been a great year for the Focused Scientific Sessions Committees—thanks to the large number of quality abstracts submitted for the meeting. There is a little something for everyone in the diverse and enlightening program. I am looking forward to at errific meeting, inat errific venue, and Ih ope to see as many of you there as possible. I also want to extend special thanks to Focused Session Chairs, their committees, and the ACVP administrative staff who truly make it all happen.
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".