Research–management partnerships: An opportunity to integrate genetics in conservation actions
Bibliographic record
Abstract
Abstract Preserving genetic diversity is a central goal in conservation biology, but there is a mismatch between the availability of genetic data and its use in conservation policy. In this study, we surveyed conservation practitioners from academic and government institutions to identify barriers preventing the use of genetic data for conservation practice and policy. Our survey data indicates that the majority of respondents are interested in using genetic tools, and many have used them in the past. Most of these genetic studies were facilitated by partnerships with academic and private organizations, which was the preferred method for integrating genetic research in practice by managers. Although much progress has been made to incorporate genetic study in conservation practice, differences in research goals, the cost of analyses and lack of specialized personnel continue to be barriers to incorporating genetic study in evaluating management actions and informing legislation. We recommend increasing the number of collaborative partnerships between genetic researchers and conservation managers to support management strategies of wild populations.
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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 | 0.110 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".