Steller’s sea cow uncertain history illustrates importance of ecological context when interpreting demographic histories from genomes
Bibliographic record
Abstract
In their recent paper entitled “ Steller’s sea cow genome suggests this species began going extinct before the arrival of Paleolithic humans ” , Sharko et al. 1 use novel genomic methods to infer the demographic history of this species. Based on a single specimen from the Commander Islands, the authors conclude that the species suffered a single catastrophic population decline approximately 400,000 years ago and was thus already on the verge of extinction well before human arrivals in the Late Pleistocene. Here we suggest their demographic assumptions warrant reinterpretation given the ecological barriers that likely structured sea cow populations along the North Pacific Rim. Our preliminary range simulations suggest that the Commander Is. population may have been physically isolated from others, making it unsuitable as a demographic inference for the entire sea cow North Pacific range. Under these assumptions, Sharko et al.’s findings are more likely indicative of the time since the isolation of this remnant population from the rest of the sea cow range, rather than representative of the population contraction of the species. This perspective highlights the importance of considering historical ecology and paleobiogeography when interpreting genomic data to infer past demographic histories.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".