A New Model of Sheep Ig Diversification: Shifting the Emphasis Toward Combinatorial Mechanisms and Away from Hypermutation
Bibliographic record
Abstract
The current model of Ig repertoire development in sheep focuses on the rearrangement of a small number (approximately 20) of Vlambda gene segments. It is believed that this limited combinatorial repertoire is then further diversified through postrearrangement somatic hypermutation. This process has been reported to introduce as many as 110 mutations/1000 nucleotides. In contrast, our data have that indicated somatic hypermutation may diversify the preimmune repertoire to a much lesser extent. We have identified 64 new Vlambda gene segments within the rearranged Ig repertoire. As a result, many of the unique nucleotide patterns thought to be the product of somatic hypermutation are actually hard-coded within the germline. We suggest that combinatorial rearrangement makes a much larger contribution, and somatic hypermutation makes a much smaller contribution to the generation of diversity within the sheep Ig repertoire than is currently acknowledged.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".