Aggregated occurrence records of the federally endangered Poweshiek skipperling (Oarisma poweshiek)
Bibliographic record
Abstract
Primary biodiversity data records that are open access and available in a standardised format are essential for conservation planning and research on policy-relevant time-scales. We created a dataset to document all known occurrence data for the Federally Endangered Poweshiek skipperling butterfly [Oarisma poweshiek (Parker, 1870; Lepidoptera: Hesperiidae)]. The Poweshiek skipperling was a historically common species in prairie systems across the upper Midwest, United States and Manitoba, Canada. Rapid declines have reduced the number of verified extant sites to six. Aggregating and curating Poweshiek skipperling occurrence records documents and preserves all known distributional data, which can be used to address questions related to Poweshiek skipperling conservation, ecology and biogeography. Over 3500 occurrence records were aggregated over a temporal coverage from 1872 to present. Occurrence records were obtained from 37 data providers in the conservation and natural history collection community using both “HumanObservation” and “PreservedSpecimen” as an acceptable basisOfRecord. Data were obtained in different formats and with differing degrees of quality control. During the data aggregation and cleaning process, we transcribed specimen label data, georeferenced occurrences, adopted a controlled vocabulary, removed duplicates and standardised formatting. We examined the dataset for inconsistencies with known Poweshiek skipperling biogeography and phenology and we verified or removed inconsistencies by working with the original data providers. In total, 12 occurrence records were removed because we identified them to be the western congener Oarisma garita (Reakirt, 1866). This resulting dataset enhances the permanency of Poweshiek skipperling occurrence data in a standardised format. This is a validated and comprehensive dataset of occurrence records for the Poweshiek skipperling (Oarisma poweshiek) utilising both observation and specimen-based records. Occurrence data are preserved and available for continued research and conservation projects using standardised Darwin Core formatting where possible. Prior to this project, much of these occurrence records were not mobilised and were being stored in individual institutional databases, researcher datasets and personal records. This dataset aggregates presence data from state conservation agencies, natural heritage programmes, natural history collections, citizen scientists, researchers and the U.S. Fish & Wildlife Service. The data include opportunistic observations and collections, research vouchers, observations collected for population monitoring and observations collected using standardised research methodologies. The aggregated occurrence records underwent cleaning efforts that improved data interoperablitity, removed transcription errors and verified or removed uncertain data. This dataset enhances available information on the spatiotemporal distribution of this Federally Endangered species. As part of this aggregation process, we discovered and verified Poweshiek skipperling occurrence records from two previously unknown states, Nebraska and Ohio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".