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
Flow cytometry is a highly sensitive and specific method for simultaneous analysis of multiple parameters of individual cells in a suspension. It has a range of applications in veterinary medicine, and it is increasingly used in veterinary oncology as more species-specific antibodies are generated and cross-reactivity of antibodies is characterized. Two major applications in veterinary oncology are (1) immunophenotyping with a panel of fluorescently labeled antibodies to assess expression of cell markers and (2) determination of the DNA content of cells with fluorescent dyes that bind nucleic acids. The diagnostic and prognostic value of classifying round cell tumors of animals-especially, lymphocyte proliferations-remains to be fully determined, but studies to date have indicated benefit to patient management. Similarly, determining the proliferating fraction of tumors through DNA analysis remains to be standardized and validated in veterinary oncology but shows promise as an adjunct to morphologic tumor classification. This article reviews technical aspects of flow cytometry, availability of antibodies suitable for studies in domestic animals, and applications in veterinary oncology with emphasis on characterization of round cell tumors.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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