Harnessing the Power of Community Science to Address Data Gaps in Arctic Observing: Invasive Species in Alaska as Case Examples
Bibliographic record
Abstract
The Arctic is undergoing large-scale changes that are likely to accelerate in future decades such as introductions and expansions of invasive species. The Arctic is in a unique position to prevent new introductions and spread of existing invasive species by adopting policies and actions aimed at early detection. Responding to threats from invasive species to minimize impacts to ecosystems, communities, food security, and northern economies will necessitate extensive observations and monitoring, but resource managers often face decisions without having adequate data and resources at hand. Local observing programs such as citizen science and community-based monitoring programs present attractive methods for increasing observing capacity that span contributory and co-created approaches while raising awareness of an issue among stakeholders. While the co-created model has been widely applied and encouraged in the Arctic context, contributory citizen science programs offer an additional tool for addressing observing needs in the Arctic. We showcase three contributory citizen science programs related to freshwater, terrestrial, and marine environments that have supported the objectives of the Alaska Invasive Species Partnership. We discuss criteria for achieving ARIAS priority actions at the participant scale related to participants’ motivation and participants’ understanding of the value of their contributions, at the programmatic scale, for example promoting accessible, reciprocal, and transparent knowledge exchange, and at the policy and science scale where management action is data driven. The approach is aimed at successful integration of citizen science into Arctic policy making. Finally, we discuss challenges related to broader global data collection and future directions for contributory citizen science within Arctic observing networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".