Blue whale (Balaenoptera musculus musculus) genome: population structure and history in the North Atlantic
Bibliographic record
Abstract
Knowledge of genetic diversity and structure is essential for developing conservation strategies for endangered species. The advances in museum genomics can assist in better understanding the effects of over-hunting on the genome by comparing historical to present-day samples. Blue whales were hunted to the point of near extinction in the mid-twentieth century. Herein, we use whole genome sequencing to elucidate the poorly understood population structure of North Atlantic (NA) blue whales (Balaenoptera musculus musculus). We generated a de novo genome assembly of 2.49 Mbp for a NA blue whale (N50 of 1.46 Mb) to analyze 19 whole genomic sequences and 28 complete mitochondrial genomes. We included present-day and historical samples (earliest from 1900) from the Atlantic and Antarctica to understand the impact of whaling on the genetic diversity. We found low population structuring, but high genetic diversity, suggesting a single, panmictic population in the NA. We identified gene flow from fin whale to blue whales, accounting for ~3.5% of the genome. Introgression between blue and fin whales was observed in all the present-day samples but were lacking in some whales sampled early in the 20th century, which suggests increasing disruption in mate choice concomitant with decline in blue whale population. We also assembled and analyzed the transcriptome and revealed positive selection of oncogenes, which may be involved in reduced cancer rates in this largest of mammals ever known. Our sequencing and population structuring studies provide a genomic framework to guide ongoing conservation strategies for this iconic species.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".