Using conservation genetics to inform reintroduction of the endangered Mottled Duskywing (Erynnis martialis)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Habitat loss and climate change have caused declines in species diversity and abundance globally, including in butterflies which are important components of many ecosystems. Reintroductions are increasingly used to reverse diversity loss but are most effective when informed using genetics. I developed 24 microsatellites and characterized genetic structure and diversity of the endangered Mottled Duskywing (Erynnis martialis) in Ontario and neighbouring provinces and states. These were used to inform a planned reintroduction in Ontario. Populations had moderate levels of genetic diversity, however all but the largest populations may be subject to appreciable levels of genetic drift. Populations more than 8 km apart appear to be isolated from each other. My work forms part of a larger effort to achieve the overall recovery of the species in Ontario. Tools I developed may be used to inform future reintroductions of the species, and to monitor status of introduced and extant populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it