The Role of Citizen Science in the Research and Management of Invasive Lionfish across the Western Atlantic
Bibliographic record
Abstract
Managing invasive Indo-Pacific lionfish (Pterois volitans and P. miles) in the Western Atlantic Ocean is beyond the capacity of natural resource organizations alone. In response, organizations have mobilized members of the public and citizen scientists to help. We used a structured survey to assess the activities and perceptions of 71 organizations that engage the public and citizen scientists in lionfish research and management throughout the invaded range of the Western Atlantic. Five case studies were also conducted that exemplified varied and multi-pronged approaches to engagement of the public and citizen scientists in lionfish control, monitoring, and knowledge-sharing. The public has been engaged to some extent in every approach, but organizations most frequently indicated engaging members of the public in raising awareness, promoting consumption, organized culling/removal, tournaments, and data collection. Sixty-five percent of organizations surveyed engaged the public in data collection, and data collection was ranked as the scientific research activity in which the public is most often involved. Most organizations indicated their data has contributed to scientific publications, management, and government agency research and/or policy. Collectively these findings demonstrate the conservation value of citizen scientists to assist organizational efforts to control, manage, and study a large-scale marine invasion.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".