Bibliographic record
Abstract
The patchy distribution of genes across the prokaryotes may be caused by multiple gene losses or lateral transfer. Probabilistic models of gene gain and loss are needed to distinguish between these possibilities. Existing models allow only single genes to be gained and lost, despite the empirical evidence for multi-gene events. We compare birth-death models (currently the only widely-used models, in which only one gene can be gained or lost at a time) to blocks models (allowing gain and loss of multiple genes within a family). We analyze two pairs of genomes: two E. coli strains, and the distantly-related Archaeoglobus fulgidus (archaea) and Bacillus subtilis (gram positive bacteria). Blocks models describe the data much better than birth-death models. Our models suggest that lateral transfers of multiple genes from the same family are rare (although transfers of single genes are probably common). For both pairs, the estimated median time that a gene will remain in the genome is not much greater than the time separating the common ancestors of the archaea and bacteria. Deep phylogenetic reconstruction from sequence data will therefore depend on choosing genes likely to remain in the genome for a long time. Phylogenies based on the blocks model are more biologically plausible than phylogenies based on the birth-death model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".