Combining Physical and Digital Data Collection for Citizen Science Climate Research
Bibliographic record
Abstract
In this paper, we present our experience designing and implementing a hybrid citizen science protocol combining local data collection reported digitally with the return of physical samples by mail. Our project, Fossil Atmospheres, housed within the Paleobiology Department of the Smithsonian Institution’s National Museum of Natural History, sought to complete a broad geographic collection of Ginkgo biloba L. leaves to better understand climate change over time. We also wished to leverage and test the affordances of using an established online platform as a technological tool for research-quality data collection. Participants were asked to find a local ginkgo tree and, using a hybrid protocol, collect leaf samples and record site data, including photos, GPS coordinates, and tree characteristics, using the iNaturalist online platform. Participants then returned their leaf samples by mail. Fossil Atmospheres received 562 leaf samples from 352 participants. These samples, representing 36 states, met our target geographic transects and reflected the known habitat range of living ginkgo in the United States. We were able to successfully pair a large majority of received samples to their corresponding digital data records, allowing us to include 88% of the samples received within the Fossil Atmospheres data set. These results greatly exceeded our project goals. The hybrid protocol model we present, based on our experiences, indicates that using tools like iNaturalist provides multiple benefits that meet or exceed more traditional data collection models, including increases in the scale of data that can be collected, data accuracy, and data completeness, uniformity, usability, and accessibility.
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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 | 0.098 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".