Disentangling the genetic consequences of demographic change
Bibliographic record
Abstract
Quantifying the impact of human activity on the capacity of populations to persist is paramount to conservation biology, as numerous species and populations have already been driven to or beyond the brink of extinction. Those populations that persist are often a sobering example of the evolutionary power of human-disturbance, such as the loss of tusks in African elephants resulting from ivory harvesting (Campbell-Staton et al., 2021) and rapid life-history evolution in northern Atlantic cod in response to fisheries (Olsen et al., 2004). These evolutionary responses reflect a delicate interplay between demographic and selective processes (e.g., evolutionary rescue: Bell & Gonzalez, 2009; Gomulkiewicz & Holt, 1995), both of which can modify genetic variation for fitness. While quantifying fitness remains a difficult challenge, generalizable insights into the evolutionary consequences of population collapse can be provided in systems with independent demographic shifts in response to human activity. Unfortunately, such was the case for sea otter populations across its range in the 18th and 19th centuries, where the fur-trade had catastrophic, range-wide effects on sea otter (Enhydra lutris) populations. In a From the Cover article in this issue of Molecular Ecology, Beichman et al. (2022) combine a population genomic spatiotemporal data set and theoretical simulations not only to quantify past demographic change in response to sea otter exploitation, but also to understand the consequences of population collapse on species persistence.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".