Conservation genetics as applied evolution: from genetic pattern to evolutionary process
Bibliographic record
Abstract
Conservation genetics can be seen as the effort to influence the evolutionary process in ways that enhance the persistence of populations. Much published research in the field applies genetic sampling techniques to infer population parameters from the patterns of variation in threatened populations. The limited resolution of these inferences seems to yield limited confidence which results in conservative policy recommendations. As an alternative, I suggest that conservation genetics focus on the relationships between those variables conservationists can control, and the probability of desirable evolutionary outcomes. This research would involve three phases - a greater use of existing evolutionary theory; testing management options using experimental evolution; and 'field trials' under an adaptive management framework. It would take a probabilistic approach that recognizes the stochasticity inherent in evolutionary change. This would allow a more nuanced approach to conservation policy than rule of thumb guidelines. Moreover, it would capitalize on the fact that evolution is a unifying theory in biology and draw on the substantial body of evolutionary knowledge that has been built up over the last half a century.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.031 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".