Patterns of community science data use in peer-reviewed research on biodiversity
Bibliographic record
Abstract
Abstract Community science (“citizen science”) represent a potentially abundant and inexpensive source of information for biodiversity research. However, analyzing such data has inherent challenges. To explore where and how community science data are translated into scientific knowledge, we conducted a literature review in a sample of 334 peer-reviewed scientific articles. Specifically, we investigated how the use of community science data varied among taxonomic groups and geographic regions, and what threats to biodiversity, if any, were examined. Community science data were used mostly for research on birds and invertebrates, and the data used were mainly from the United States and the United Kingdom. Literature in certain countries used a wider breadth of projects, while others made repeated use of comparably fewer datasets. Community science efforts were largely used to measure abundance, trends, distributions, and range shifts. However, few articles linked these metrics to any particular threats to biodiversity. Furthermore, community science data were used infrequently for research on threatened species and limited mostly to count data rather than collecting more specific information such as life history, phenological or genetic data, suggesting that community science may be underutilized for these key aspects of biodiversity conservation. We conclude that even with the rise of community science data use in research, there remains tremendous potential to better use these existing datasets for biodiversity research.
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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 | 0.169 | 0.523 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.072 | 0.071 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".