The Value of Citizen Science in Increasing Our Knowledge of Under-Sampled Biodiversity: An Overview of Public Documentation of Auchenorrhyncha and the Hoppers of North Carolina
Bibliographic record
Abstract
Due to the increasing popularity of websites specializing in nature documentation, there has been a surge in the number of people enthusiastic about observing and documenting nature over the past 2 decades. These citizen scientists are recording biodiversity on unprecedented temporal and spatial scales, rendering data of tremendous value to the scientific community. In this study, we investigate the role of citizen science in increasing knowledge of global biodiversity through the examination of notable contributions to the understanding of the insect suborder Auchenorrhyncha, also known as true hoppers, in North America. We have compiled a comprehensive summary of citizen science contributions—published and unpublished—to the understanding of hopper diversity, finding over fifty previously unpublished country and state records as well as dozens of undescribed and potentially undescribed species. We compare citizen science contributions to those published in the literature as well as specimen records in collections in the United States and Canada, illuminating the fact that the copious data afforded by citizen science contributions are underutilized. We also introduce the websiteHoppers of North Carolina, a revolutionary new benchmark for tracking hopper diversity, disseminating knowledge from the literature, and incorporating citizen science. Finally, we provide a series of recommendations for both the entomological community and citizen science platforms on how best to approach, utilize, and increase the quality of sightings from the general public.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".