A novel application of ecological analyses to assess transposable element distributions in the genome of the domestic cow, <i>Bos taurus</i>
Bibliographic record
Abstract
Transposable elements (TEs) are among the most abundant components of many eukaryotic genomes. Efforts to explain TE abundance, as well as TE diversity among genomes, have led some researchers to draw an analogy between genomic and ecological processes. Adopting this perspective, we conducted an analysis of the cow (Bos taurus) genome using techniques developed by community ecologists to determine whether environmental factors influence community composition. Specifically, each chromosome within the Bos taurus genome was treated as a "linear transect", and a multivariate redundancy analysis (RDA) was used to identify large-scale spatial patterns in TE communities associated with 10 TE families. The position of each TE community on the chromosome accounted for ∼50% of the variation along the chromosome "transect". Multivariate analysis further revealed an effect of gene density on TE communities that is influenced by several other factors in the (genomic) environment, including chromosome length and TE density. The results of this analysis demonstrate that ecological methods can be applied successfully to help answer genomic questions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 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".