Citizen Science Association Data & Metadata Working Group: Report from CSA 2017 and Future Outlook
Bibliographic record
Abstract
In 2016, the U.S. National Park Service (NPS) celebrated its centennial anniversary with over 100 Bioblitzes hosted in parks around the country. Because these events engaged local communities in documenting their natural environment, each park was given the freedom to decide how their celebration should unfold, for example by specifying which species to document or when to collect information. Still, all parks across the United States used the iNaturalist1 mobile application for data collection. Because iNaturalist collects and stores biodiversity data in line with the Darwin Core standard, information collected in each local park can be verified by a community of experts and shared with GBIF,2 a global database of biodiversity observations. This case study illustrates how — through interoperable data standards — information considered important by local communities can “scale” to be used in national or global research and policymaking. The goal of the Citizen Science Association (CSA) Data and Metadata Working Group (WG) is to make this vision a reality by promoting interoperability not just in one research domain like biodiversity, but across each and every research domain where citizen science is taking root and growing.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 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".