On the association between chromosomal rearragements and genic evolution in mammals
Bibliographic record
Abstract
The main objectives of this work are: a) To test the predictions of suppressed-recombination chromosomal speciation models on two different lineages of mammals: rodents and rimates. Suppressed-recombination chromosomal speciation is still quite elusive as a mode of speciation in mammals. Experimental results are scarce and the first objective of this work is to analyze whole-genome data looking for traces of events of chromosomal speciation. Rodent and primate lineages were chosen for this search, not just because of their particular biological and cytological characteristics, which make them good candidates to have speciated by this mechanism, but also because they were the first mammalian organisms to be fully sequenced. b) To study the effects of chromosomal rearrangements on genic evolutionary rates. As have been seen in the introduction, there are many of potential interactions among chromosomal rearrangements and evolutionary rates, so the second goal of this work was to try to understand the impact of chromosomal rearrangements over substitution rates by means of other mechanisms not related with speciation. c) To distinguish individual contributions of different genomic factors in the potential association among chromosomal rearrangements and evolutionary rates.The third main goal of this thesis was to discern among the different factors that could be explaining the many associations between chromosomal and genic evolution that were detected in different studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".