Management practices benefit endangered Poweshiek skipperling (<i>Oarisma poweshiek</i>) in Manitoba tall grass prairie
Bibliographic record
Abstract
Poweshiek skipperling ( Oarisma poweshiek Parker, 1870) populations have declined precipitously in the past few decades, and their global range is now restricted to two isolated regions, one of which is the managed Manitoba tall grass prairie in Canada. In this paper, we used a decade of survey data from 2010 to 2019 to understand how habitat features, management practices, and extreme weather impact Poweshiek skipperling abundance in Manitoba. The strongest predictor of abundance was the density of black-eyed Susans ( Rudbeckia hirta L.), a primary nectar plant for adults. Poweshiek skipperling abundance also had a negative relationship with both the number of years since a burn occurred and the number of years since grazing occurred. Cumulative precipitation during their active period (May–June) had a negative relationship with skipperling abundance, whereas warm early springs and cool temperatures during the active period had positive relationships. These results suggest that management actions that maintain tall grass prairie habitat in an early successional stage (burning and grazing) and maintain important nectar sources benefit this population. In contrast, extreme weather events had varying effects on Poweshiek skipperling abundance. Results from this study inform ongoing management practices in the Manitoba tall grass prairie to support this endangered population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".