The risks and rewards of community science for threatened species monitoring
Bibliographic record
Abstract
Abstract Finding ways of efficiently monitoring threatened species can be critical to effective conservation. The global proliferation of community science (also called citizen science) programs, like iNaturalist, presents a potential alternative or complement to conventional threatened species monitoring. Using a case study of ~700,000 observations of >10,000 IUCN Red List Threatened species within iNaturalist observations, we illustrate the potential risks and rewards of using community science to monitor threatened species. Poor data quality and risks of sending untrained volunteers to sample species that are sensitive to disturbance or harvesting are key barriers to overcome. Yet community science can expand the breadth of monitoring at little extra cost, while indirectly benefiting conservation through outreach and education. We conclude with a list of actionable recommendations to further mitigate the risks and capitalize on the rewards of community science as a threatened species monitoring tool.
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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 | 0.072 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".