Bibliographic record
Abstract
There has been a trend in increasing the phylogenetic scope of genome sequencing while decreasing the quality of the published sequence for each genome. With reduced finishing effort, there is an increasing number of genomes being published in contig form. Rearrangement algorithms, including gene order-based phylogenetic tools, require whole genome data on gene order, segment order, or some other marker order. Items whose chromosomal location is unknown cannot be part of the input. The question we address here is, for gene order-based phylogenetic analysis, how can we use rearrangement algorithms to handle genomes available in contig form only? Our suggestion is to use the contigs directly in the rearrangement algorithms as if they were chromosomes, while making a number of corrections, e.g., we correct for the number of extra fusion/fission operations required to make contigs comparable to full assemblies. We model the relationship between contig number and genomic distance, and estimate the parameters of this model using insect genome data. With this model, we use distance matrix methods to reconstruct the phylogeny based on genomic distance and numbers of contigs. We compare this with methods to reconstruct ancestral gene orders using uncorrected contig data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".