Comparison of responses by bacteriophages and bacteria to pressures on the base composition of open reading frames.
Bibliographic record
Abstract
Differences in the base composition of genomes can occur because of GC pressure, purine-loading pressure (AG pressure) and RNY pressure, for which there are possible functional explanations, and because of the more abstract pressures exerted by individual bases. The graphical approach of Muto and Osawa was used to analyse how bacteriophages and bacteria balance potentially conflicting pressures on their genomes. Phages generally respond to AG pressure by increasing A while keeping T constant, and by decreasing C while keeping G constant. In contrast, bacteria generally increase both A and T, the former more so, and decrease both G and C, the latter more so. These differences largely occur at third codon positions, which are more responsive than first and second codon positions to AG pressure and GC pressure. Phages respond to AG pressure more in the third codon position than bacteria, whereas bacteria respond more in the first codon position than phages. Conversely, bacteria respond to GC pressure more in the third codon position than phages, whereas phages respond more in the first codon position than bacteria. As GC pressure increases, A is traded for C and AG pressure decreases; first and second codon positions, having more A than T, are most responsive to this negative effect of increased GC pressure; third positions either do not respond (phages) or respond weakly (bacteria). In a set of 48 phage-host pairs, degrees of purine loading were less correlated between phage and host than were GC percentages. These results suggest that pressures on conventional and genome phenotypes operate differentially in phages and bacteria, generating both general differences in base composition and specific differences characteristic of particular phage-host pairs. The reciprocal relationship between GC pressure and AG pressure implies that effects attributed to GC pressure may actually be due to AG pressure, and vice versa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".