The Caddisfly Collective: Methods of assessing Trichoptera diversity on a continental scale with community scientists
Bibliographic record
Abstract
Amidst a global biodiversity crisis, collecting data at large spatial scales can illuminate patterns. Community science can be an avenue to reduce costs, broaden the scope of sampling, and, most importantly, connect with members of the public who are interested in and impacted by long-term ecological change. In 2021, we formulated a community science project – The Caddisfly Collective. Our goal was to study the regional influences on the responses of stream caddisfly (Trichoptera) communities to urbanization in the United States and Canada. Community scientists helped us achieve this goal by collecting caddisflies across a wider geographic scale than we could have reached on our own. To build The Caddisfly Collective, we recruited participants through social media and other online forums. We mailed collecting kits with a USB-powered ultraviolet LED light, a collecting container, bottles of preservative, data sheets, and collection labels to each participant; participants mailed back specimens and completed data sheets. There was a 79.7% rate of follow-through from sign-up to collection. During the project, 63 participants set up light-traps near urban and non-urban streams in seven different North American geographic regions, collecting adult caddisflies at 141 sites across the United States and Canada. Most sites were in the Midwest region, while the fewest sites were in the Far North region. Urban areas, classified by land cover data, comprised ~29% of total sites. We hope the details of our project can help other interested scientists implement similar projects in the future, especially focused on ecologically important caddisfly communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".