Using Citizen Science to Document Biodiversity on a University Campus: A Year-Long Case Study
Bibliographic record
Abstract
Citizen science is a rapidly growing field, particularly among young scientists. In this case study, we review a year-long citizen science initiative hosted at Western University, Canada, which aimed to document and highlight biodiversity on campus while simultaneously seeking to improve community engagement with the environment. Using the popular citizen science platform iNaturalist, we facilitated data collection and community engagement through a combination of informal field surveys, undergraduate-level course assignments, social media, and passive data submission. Throughout the first year of the initiative, nearly 300 community members submitted 3716 observations of 1225 species, including observations of 103 species documented on iNaturalist for the first time in the region, and other species of ecological significance. This citizen science project underscores the strengths and utility of citizen science and provides a framework for other higher education institutions to develop similar initiatives.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".