On the origin of species: insights from the ecological genomics of lake whitefish
Bibliographic record
Abstract
In contrast to the large amount of ecological information supporting the role of natural selection as a main cause of population divergence and speciation, an understanding of the genomic basis underlying those processes is in its infancy. In this paper, we review the main findings of a long-term research programme that we have been conducting on the ecological genomics of sympatric forms of whitefish (Coregonus spp.) engaged in the process of speciation. We present this system as an example of how applying a combination of approaches under the conceptual framework of the theory of adaptive radiation has yielded substantial insight into evolutionary processes in a non-model species. We also discuss how the joint use of recent biotechnological developments will provide a powerful means to address issues raised by observations made to date. Namely, we present data illustrating the potential offered by combining next generation sequencing technologies with other genomic approaches to reveal the genomic bases of adaptive divergence and reproductive isolation. Given increasing access to these new genomic tools, we argue that non-model species studied in their ecological context such as whitefish will play an increasingly important role in generalizing knowledge of speciation.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".