Bibliographic record
Abstract
Knowledge of the geographical distribution of feline immunodeficiency virus (FIV) subtypes is important for understanding different disease courses and for vaccine design. Intersubtype recombination may develop in areas where more than one subtype is prevalent and has the potential to create new transmittable variants with novel pathogenic properties. In this study, 40 FIV-positive DNA samples were classified by sequence analysis of the LTR–gag region. Phylogenetic analysis indicated that 32 Canadian FIV isolates clustered with previously identified subtypes A, B and C and that subtype A was most frequent in Ontario. Four strains with inconsistent clade assignment were further analysed by sequencing of the env–LTR regions. Comparisons of phylogenetic trees constructed from the two different regions of the genome and analysis of similarities to reference sequences yielded classification of three samples as A/B and one as A/C intersubtype recombinants. Although the A/B recombinant samples were obtained from unrelated cats in geographically disparate regions, a common breakpoint was consistently identified within gag. In addition, there was no evidence of co-infection with parental strains of subtypes A and B as indicated by PCR-based limiting dilution assays, although these assays allowed for the identification of two different recombinant viruses co-existing in one sample. Both sequences contained the same breakpoint. These findings suggested that a new circulating recombinant FIV may be enzootic in Ontario.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.853 | 0.611 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".