Draft genome sequence of the silver pomfret fish, <i>Pampus argenteus</i>
Bibliographic record
Abstract
Silver pomfret, Pampus argenteus, is a fish species from coastal waters. Despite its high commercial value, this edible fish has not been sequenced. Hence, its genetic and genomic studies have been limited. We report the first draft genome sequence of the silver pomfret obtained using a Next Generation Sequencing (NGS) technology. We assembled 38.7 Gb of nucleotides into scaffolds of 350 Mb with N50 of about 1.5 kb, using high quality paired end reads. These scaffolds represent 63.7% of the estimated silver pomfret genome length. The newly sequenced and assembled genome has 11.06% repetitive DNA regions, and this percentage is comparable to that of the tilapia genome. The genome analysis predicted 16 322 genes. About 91% of these genes showed homology with known proteins. Many gene clusters were annotated to protein and fatty-acid metabolism pathways that may be important in the context of the meat texture and immune system developmental processes. The reference genome can pave the way for the identification of many other genomic features that could improve breeding and population-management strategies, and it can also help characterize the genetic diversity of P. argenteus.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".