AnAms1.0: A high-quality chromosome-scale assembly of a domestic cat <i>Felis catus</i> of American Shorthair breed
Bibliographic record
Abstract
Abstract The domestic cat ( Felis catus ) is one of the most popular companion animals in the world. Comprehensive genomic resources will aid the development and application of veterinary medicine including to improve feline health, in particular, to enable precision medicine which is promising in human application. However, currently available cat genome assemblies were mostly built based on the Abyssinian cat breed which is highly inbred and has limited power in representing the vast diversity of the cat population. Moreover, the current reference assembly remains fragmented with sequences contained in thousands of scaffolds. We constructed a reference-grade chromosome-scale genome assembly of a domestic cat, Felis catus genome of American Shorthair breed, Anicom American shorthair 1.0 (AnAms1.0) with high contiguity (scaffold N50 > 120 Mb), by combining multiple advanced genomic technologies, including PacBio long-read sequencing as well as sequence scaffolding by long-range genomic information obtained from Hi-C and optical mapping data. Homology-based and ab initio gene annotation was performed with the Iso-Seq data. Analyzed data is be publicly accessible on Cats genome informatics (Cats-I, https://cat.annotation.jp/ ), a cat genome database established as a platform to facilitate the accumulation and sharing of genomic resources to improve veterinary care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".