Scientific Report First name / Family name Murray PATTERSON Nationality Canadian Name of the Host Organisation INRIA First Name / family name
Bibliographic record
Abstract
the fellowship was to extend the algorithm of [2] to include horizontal gene transfers as detected in [1]. We now have a working implementation of this extended algorithm, which shows promising results when applied to datasets of cyanobacterial genome evolution (that involve horizontal gene transfer) generated by several methods, including that of [3]. This method is the first to reconstruct ancestral gene structures, explicitly handling horizontal transfers, which are very numerous in all unicellular organisms. This is important, since unicellular organisms represent more than 90 % of the historial biodiversity on earth, and are suspected to allow for rapid evolution in microbes, in particular, resistance to antibiotics, so it is essential to understanding the evolution of life. The next immediate step is to apply this method to the many other datasets (cyanobacterial, and beyond) in order to better understand how genes are co-evolving in the presence of horizontal gene transfer. After using the method above to reconstruct ancestral genome structures, given a dataset of genome evolution as inferred by a method such as that of [3], one way to assess the quality of this dataset is by how close the ancestral genome structure is to being linear
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.529 | 0.385 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".