Combining community science and taxonomist expertise for large‐scale monitoring of insect pollinators: Perspective and insights from <i>Abeilles citoyennes</i>
Bibliographic record
Abstract
Abstract While evidence of insect pollinator declines accumulates, little is known about the pollinator communities that are most vulnerable to population fluctuations and may require conservation actions. Among the main reasons for this lack of knowledge about the status and trends of native pollinators are the time, cost, and expertise required to collect and identify wild insect pollinators (bees, more specifically). Here, we discuss how leveraging the complementarity of community science and taxonomist expertise can help overcome these challenges and provide perspective and insights from launching the large‐scale monitoring program Abeilles citoyennes . The overall objective of this community science project is to monitor wild bee (Apoidea) and hover fly (Syrphidae) diversity in the province of Quebec, Canada, and study the effects of landscape composition on their communities. From 2019 to 2021, 131 volunteers collected insects at 161 sites across the province. A total of 13,558 bees and 2,486 hover flies were collected and identified to species. The project protocol and potential data uses are presented, along with a discussion of the benefits and challenges of using an expert‐assisted community science approach for pollinator monitoring and opportunities for improvement.
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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 | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".