Scientific contributions of citizen science applied to rare or threatened animals
Bibliographic record
Abstract
Citizen science is filling important monitoring gaps and thus contributing to the conservation of rare or threatened animals. However, most researchers have used peer-reviewed publications to evaluate citizen science contributions. We quantified a larger spectrum of citizen science's contributions to the monitoring of rare or threatened animals, including contributions to the peer-reviewed publications, gray literature and to conservation measures (i.e., actions taken as a direct result of citizen science monitoring). We sought to provide broad information on how results of studies of citizen science monitoring is used. We also evaluated factors associated with success of citizen science projects. We conducted a web search to find citizen science projects focusing on rare and threatened species and surveyed citizen science project managers about their contributions and factors influencing their success. The number of projects increased rapidly after 2010. Almost one-half of the citizen science projects produced at least 1 peer-reviewed publication, 64% produced at least 1 gray literature publication, and 64% resulted in at least 1 conservation measure. Conservation measures covered a wide range of actions, including management and mitigation plans, modification of threat status, identification and establishment of protected areas, habitat restoration, control of invasive species, captive breeding programs, and awareness campaigns. Longevity, data quality, and collaboration type best explained quantities of all types of scientific contributions of citizen science. We found that citizen science contributed substantially to knowledge advancement and conservation, especially when programs were long term and had rigorous data collection and management standards, and multidisciplinary or transdisciplinary collaborations.
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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 | 0.164 | 0.424 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.040 | 0.041 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".