A continuum of genetic mixing for conservation management along the (mal)adaptation spectrum: A comment on Hoffmann et al.
Bibliographic record
Abstract
When restoring gene flow for conservation management, genetic variation should be viewed along a continuum of genetic divergence between donor and recipient populations. On the one hand, maintaining local adaptation (low divergence between donors and recipients) can enhance conservation success in the short term. On the other hand, reducing local adaptation in the short term by increasing genetic diversity (high divergence between some donors and recipients) might have better long-term success in the face of changing environmental conditions. Both Hoffman et al. (2020) and a paper we previously published in a Special Issue on Maladaptation in Applied Conservation (Derry et al., 2019) provide frameworks and syntheses for how best to apply conservation strategies in light of genetic variation and adaptation. A key difference between these two studies was that whereas Derry et al. (2019) performed a quantitative meta-analysis, Hoffman et al. (2020) relied on case studies and theoretical considerations, yielding slightly different conclusions. We here provide a summary of the two studies and contrast of the main similarities and differences between them, while highlighting terminology used to describe and explain main concepts.
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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.050 | 0.083 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".